Repository: kentonl/e2e-coref
Branch: master
Commit: 9d1ee1972f6e
Files: 24
Total size: 94.5 KB

Directory structure:
gitextract_zs1s9vox/

├── .gitignore
├── LICENSE
├── README.md
├── cache_elmo.py
├── conll.py
├── continuous_evaluate.py
├── coref_kernels.cc
├── coref_model.py
├── coref_ops.py
├── demo.py
├── evaluate.py
├── experiments.conf
├── filter_embeddings.py
├── get_char_vocab.py
├── metrics.py
├── minimize.py
├── predict.py
├── ps.py
├── requirements.txt
├── setup_all.sh
├── setup_training.sh
├── train.py
├── util.py
└── worker.py

================================================
FILE CONTENTS
================================================

================================================
FILE: .gitignore
================================================
*.pyc
*.so
*.jsonlines
logs
conll-2012
char_vocab*.txt
glove*.txt
glove*.txt.filtered
*.v*_*_conll
*.hdf5

================================================
FILE: LICENSE
================================================
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================================================
FILE: README.md
================================================
# Higher-order Coreference Resolution with Coarse-to-fine Inference

## Introduction
This repository contains the code for replicating results from

* [Higher-order Coreference Resolution with Coarse-to-fine Inference](https://arxiv.org/abs/1804.05392)
* [Kenton Lee](http://kentonl.com/), [Luheng He](https://homes.cs.washington.edu/~luheng), and [Luke Zettlemoyer](https://www.cs.washington.edu/people/faculty/lsz)
* In NAACL 2018

## Getting Started

* Install python (either 2 or 3) requirements: `pip install -r requirements.txt`
* Download pretrained models at https://drive.google.com/file/d/1fkifqZzdzsOEo0DXMzCFjiNXqsKG_cHi
  * Move the downloaded file to the root of the repo and extract: `tar -xzvf e2e-coref.tgz`
* Download GloVe embeddings and build custom kernels by running `setup_all.sh`.
  * There are 3 platform-dependent ways to build custom TensorFlow kernels. Please comment/uncomment the appropriate lines in the script.
* To train your own models, run `setup_training.sh`
  * This assumes access to OntoNotes 5.0. Please edit the `ontonotes_path` variable.

## Training Instructions

* Experiment configurations are found in `experiments.conf`
* Choose an experiment that you would like to run, e.g. `best`
* Training: `python train.py <experiment>`
* Results are stored in the `logs` directory and can be viewed via TensorBoard.
* Evaluation: `python evaluate.py <experiment>`

## Demo Instructions

* Command-line demo: `python demo.py final`
* To run the demo with other experiments, replace `final` with your configuration name.

## Batched Prediction Instructions

* Create a file where each line is in the following json format (make sure to strip the newlines so each line is well-formed json):
```
{
  "clusters": [],
  "doc_key": "nw",
  "sentences": [["This", "is", "the", "first", "sentence", "."], ["This", "is", "the", "second", "."]],
  "speakers": [["spk1", "spk1", "spk1", "spk1", "spk1", "spk1"], ["spk2", "spk2", "spk2", "spk2", "spk2"]]
}
```
  * `clusters` should be left empty and is only used for evaluation purposes.
  * `doc_key` indicates the genre, which can be one of the following: `"bc", "bn", "mz", "nw", "pt", "tc", "wb"`
  * `speakers` indicates the speaker of each word. These can be all empty strings if there is only one known speaker.
* Run `python predict.py <experiment> <input_file> <output_file>`, which outputs the input jsonlines with predicted clusters.

## Other Quirks

* It does not use GPUs by default. Instead, it looks for the `GPU` environment variable, which the code treats as shorthand for `CUDA_VISIBLE_DEVICES`.
* The training runs indefinitely and needs to be terminated manually. The model generally converges at about 400k steps.


================================================
FILE: cache_elmo.py
================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import numpy as np
import tensorflow as tf
import tensorflow_hub as hub
import h5py
import json
import sys

def build_elmo():
  token_ph = tf.placeholder(tf.string, [None, None])
  len_ph = tf.placeholder(tf.int32, [None])
  elmo_module = hub.Module("https://tfhub.dev/google/elmo/2")
  lm_embeddings = elmo_module(
      inputs={"tokens": token_ph, "sequence_len": len_ph},
      signature="tokens", as_dict=True)
  word_emb = lm_embeddings["word_emb"]
  lm_emb = tf.stack([tf.concat([word_emb, word_emb], -1),
                     lm_embeddings["lstm_outputs1"],
                     lm_embeddings["lstm_outputs2"]], -1)
  return token_ph, len_ph, lm_emb

def cache_dataset(data_path, session, token_ph, len_ph, lm_emb, out_file):
  with open(data_path) as in_file:
    for doc_num, line in enumerate(in_file.readlines()):
      example = json.loads(line)
      sentences = example["sentences"]
      max_sentence_length = max(len(s) for s in sentences)
      tokens = [[""] * max_sentence_length for _ in sentences]
      text_len = np.array([len(s) for s in sentences])
      for i, sentence in enumerate(sentences):
        for j, word in enumerate(sentence):
          tokens[i][j] = word
      tokens = np.array(tokens)
      tf_lm_emb = session.run(lm_emb, feed_dict={
          token_ph: tokens,
          len_ph: text_len
      })
      file_key = example["doc_key"].replace("/", ":")
      group = out_file.create_group(file_key)
      for i, (e, l) in enumerate(zip(tf_lm_emb, text_len)):
        e = e[:l, :, :]
        group[str(i)] = e
      if doc_num % 10 == 0:
        print("Cached {} documents in {}".format(doc_num + 1, data_path))

if __name__ == "__main__":
  token_ph, len_ph, lm_emb = build_elmo()
  with tf.Session() as session:
    session.run(tf.global_variables_initializer())
    with h5py.File("elmo_cache.hdf5", "w") as out_file:
      for json_filename in sys.argv[1:]:
        cache_dataset(json_filename, session, token_ph, len_ph, lm_emb, out_file)


================================================
FILE: conll.py
================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import re
import os
import sys
import json
import tempfile
import subprocess
import operator
import collections

BEGIN_DOCUMENT_REGEX = re.compile(r"#begin document \((.*)\); part (\d+)")
COREF_RESULTS_REGEX = re.compile(r".*Coreference: Recall: \([0-9.]+ / [0-9.]+\) ([0-9.]+)%\tPrecision: \([0-9.]+ / [0-9.]+\) ([0-9.]+)%\tF1: ([0-9.]+)%.*", re.DOTALL)

def get_doc_key(doc_id, part):
  return "{}_{}".format(doc_id, int(part))

def output_conll(input_file, output_file, predictions):
  prediction_map = {}
  for doc_key, clusters in predictions.items():
    start_map = collections.defaultdict(list)
    end_map = collections.defaultdict(list)
    word_map = collections.defaultdict(list)
    for cluster_id, mentions in enumerate(clusters):
      for start, end in mentions:
        if start == end:
          word_map[start].append(cluster_id)
        else:
          start_map[start].append((cluster_id, end))
          end_map[end].append((cluster_id, start))
    for k,v in start_map.items():
      start_map[k] = [cluster_id for cluster_id, end in sorted(v, key=operator.itemgetter(1), reverse=True)]
    for k,v in end_map.items():
      end_map[k] = [cluster_id for cluster_id, start in sorted(v, key=operator.itemgetter(1), reverse=True)]
    prediction_map[doc_key] = (start_map, end_map, word_map)

  word_index = 0
  for line in input_file.readlines():
    row = line.split()
    if len(row) == 0:
      output_file.write("\n")
    elif row[0].startswith("#"):
      begin_match = re.match(BEGIN_DOCUMENT_REGEX, line)
      if begin_match:
        doc_key = get_doc_key(begin_match.group(1), begin_match.group(2))
        start_map, end_map, word_map = prediction_map[doc_key]
        word_index = 0
      output_file.write(line)
      output_file.write("\n")
    else:
      assert get_doc_key(row[0], row[1]) == doc_key
      coref_list = []
      if word_index in end_map:
        for cluster_id in end_map[word_index]:
          coref_list.append("{})".format(cluster_id))
      if word_index in word_map:
        for cluster_id in word_map[word_index]:
          coref_list.append("({})".format(cluster_id))
      if word_index in start_map:
        for cluster_id in start_map[word_index]:
          coref_list.append("({}".format(cluster_id))

      if len(coref_list) == 0:
        row[-1] = "-"
      else:
        row[-1] = "|".join(coref_list)

      output_file.write("   ".join(row))
      output_file.write("\n")
      word_index += 1

def official_conll_eval(gold_path, predicted_path, metric, official_stdout=False):
  cmd = ["conll-2012/scorer/v8.01/scorer.pl", metric, gold_path, predicted_path, "none"]
  process = subprocess.Popen(cmd, stdout=subprocess.PIPE)
  stdout, stderr = process.communicate()
  process.wait()

  stdout = stdout.decode("utf-8")
  if stderr is not None:
    print(stderr)

  if official_stdout:
    print("Official result for {}".format(metric))
    print(stdout)

  coref_results_match = re.match(COREF_RESULTS_REGEX, stdout)
  recall = float(coref_results_match.group(1))
  precision = float(coref_results_match.group(2))
  f1 = float(coref_results_match.group(3))
  return { "r": recall, "p": precision, "f": f1 }

def evaluate_conll(gold_path, predictions, official_stdout=False):
  with tempfile.NamedTemporaryFile(delete=False, mode="w") as prediction_file:
    with open(gold_path, "r") as gold_file:
      output_conll(gold_file, prediction_file, predictions)
    print("Predicted conll file: {}".format(prediction_file.name))
  return { m: official_conll_eval(gold_file.name, prediction_file.name, m, official_stdout) for m in ("muc", "bcub", "ceafe") }


================================================
FILE: continuous_evaluate.py
================================================
#!/usr/bin/env python
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os
import re
import time
import shutil

import tensorflow as tf
import coref_model as cm
import util

def copy_checkpoint(source, target):
  for ext in (".index", ".data-00000-of-00001"):
    shutil.copyfile(source + ext, target + ext)

if __name__ == "__main__":
  config = util.initialize_from_env()
  model = cm.CorefModel(config)

  saver = tf.train.Saver()
  log_dir = config["log_dir"]
  writer = tf.summary.FileWriter(log_dir, flush_secs=20)
  evaluated_checkpoints = set()
  max_f1 = 0
  checkpoint_pattern = re.compile(".*model.ckpt-([0-9]*)\Z")

  with tf.Session() as session:
    while True:
      ckpt = tf.train.get_checkpoint_state(log_dir)
      if ckpt and ckpt.model_checkpoint_path and ckpt.model_checkpoint_path not in evaluated_checkpoints:
        print("Evaluating {}".format(ckpt.model_checkpoint_path))

        # Move it to a temporary location to avoid being deleted by the training supervisor.
        tmp_checkpoint_path = os.path.join(log_dir, "model.tmp.ckpt")
        copy_checkpoint(ckpt.model_checkpoint_path, tmp_checkpoint_path)

        global_step = int(checkpoint_pattern.match(ckpt.model_checkpoint_path).group(1))
        saver.restore(session, ckpt.model_checkpoint_path)

        eval_summary, f1 = model.evaluate(session)

        if f1 > max_f1:
          max_f1 = f1
          copy_checkpoint(tmp_checkpoint_path, os.path.join(log_dir, "model.max.ckpt"))

        print("Current max F1: {:.2f}".format(max_f1))

        writer.add_summary(eval_summary, global_step)
        print("Evaluation written to {} at step {}".format(log_dir, global_step))

        evaluated_checkpoints.add(ckpt.model_checkpoint_path)
        sleep_time = 60
      else:
        sleep_time = 10
      print("Waiting for {} seconds before looking for next checkpoint.".format(sleep_time))
      time.sleep(sleep_time)


================================================
FILE: coref_kernels.cc
================================================
#include <map>

#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/shape_inference.h"
#include "tensorflow/core/framework/op_kernel.h"

using namespace tensorflow;

REGISTER_OP("ExtractSpans")
.Input("span_scores: float32")
.Input("candidate_starts: int32")
.Input("candidate_ends: int32")
.Input("num_output_spans: int32")
.Input("max_sentence_length: int32")
.Attr("sort_spans: bool")
.Output("output_span_indices: int32");

class ExtractSpansOp : public OpKernel {
public:
  explicit ExtractSpansOp(OpKernelConstruction* context) : OpKernel(context) {
    OP_REQUIRES_OK(context, context->GetAttr("sort_spans", &_sort_spans));
  }

  void Compute(OpKernelContext* context) override {
    TTypes<float>::ConstMatrix span_scores = context->input(0).matrix<float>();
    TTypes<int32>::ConstMatrix candidate_starts = context->input(1).matrix<int32>();
    TTypes<int32>::ConstMatrix candidate_ends = context->input(2).matrix<int32>();
    TTypes<int32>::ConstVec num_output_spans = context->input(3).vec<int32>();
    int max_sentence_length = context->input(4).scalar<int32>()();

    int num_sentences = span_scores.dimension(0);
    int num_input_spans = span_scores.dimension(1);
    int max_num_output_spans = 0;
    for (int i = 0; i < num_sentences; i++) {
      if (num_output_spans(i) > max_num_output_spans) {
        max_num_output_spans = num_output_spans(i);
      }
    }

    Tensor* output_span_indices_tensor = nullptr;
    TensorShape output_span_indices_shape({num_sentences, max_num_output_spans});
    OP_REQUIRES_OK(context, context->allocate_output(0, output_span_indices_shape,
                                                     &output_span_indices_tensor));
    TTypes<int32>::Matrix output_span_indices = output_span_indices_tensor->matrix<int32>();

    std::vector<std::vector<int>> sorted_input_span_indices(num_sentences,
                                                            std::vector<int>(num_input_spans));
    for (int i = 0; i < num_sentences; i++) {
      std::iota(sorted_input_span_indices[i].begin(), sorted_input_span_indices[i].end(), 0);
      std::sort(sorted_input_span_indices[i].begin(), sorted_input_span_indices[i].end(),
                [&span_scores, &i](int j1, int j2) {
                  return span_scores(i, j2) < span_scores(i, j1);
                });
    }

    for (int l = 0; l < num_sentences; l++) {
      std::vector<int> top_span_indices;
      std::unordered_map<int, int> end_to_earliest_start;
      std::unordered_map<int, int> start_to_latest_end;

      int current_span_index = 0,
          num_selected_spans = 0;
      while (num_selected_spans < num_output_spans(l) && current_span_index < num_input_spans) {
        int i = sorted_input_span_indices[l][current_span_index];
        bool any_crossing = false;
        const int start = candidate_starts(l, i);
        const int end = candidate_ends(l, i);
        for (int j = start; j <= end; ++j) {
          auto latest_end_iter = start_to_latest_end.find(j);
          if (latest_end_iter != start_to_latest_end.end() && j > start && latest_end_iter->second > end) {
            // Given (), exists [], such that ( [ ) ]
            any_crossing = true;
            break;
          }
          auto earliest_start_iter = end_to_earliest_start.find(j);
          if (earliest_start_iter != end_to_earliest_start.end() && j < end && earliest_start_iter->second < start) {
            // Given (), exists [], such that [ ( ] )
            any_crossing = true;
            break;
          }
        }
        if (!any_crossing) {
          if (_sort_spans) {
            top_span_indices.push_back(i);
          } else {
            output_span_indices(l, num_selected_spans) = i;
          }
          ++num_selected_spans;
          // Update data struct.
          auto latest_end_iter = start_to_latest_end.find(start);
          if (latest_end_iter == start_to_latest_end.end() || end > latest_end_iter->second) {
            start_to_latest_end[start] = end;
          }
          auto earliest_start_iter = end_to_earliest_start.find(end);
          if (earliest_start_iter == end_to_earliest_start.end() || start < earliest_start_iter->second) {
            end_to_earliest_start[end] = start;
          }
        }
        ++current_span_index;
      }
      // Sort and populate selected span indices.
      if (_sort_spans) {
        std::sort(top_span_indices.begin(), top_span_indices.end(),
                  [&candidate_starts, &candidate_ends, &l] (int i1, int i2) {
                    if (candidate_starts(l, i1) < candidate_starts(l, i2)) {
                      return true;
                    } else if (candidate_starts(l, i1) > candidate_starts(l, i2)) {
                      return false;
                    } else if (candidate_ends(l, i1) < candidate_ends(l, i2)) {
                      return true;
                    } else if (candidate_ends(l, i1) > candidate_ends(l, i2)) {
                      return false;
                    } else {
                      return i1 < i2;
                    }
                  });
        for (int i = 0; i < num_output_spans(l); ++i) {
          output_span_indices(l, i) = top_span_indices[i];
        }
      }
      // Pad with the first span index.
      for (int i = num_selected_spans; i < max_num_output_spans; ++i) {
        output_span_indices(l, i) = output_span_indices(l, 0);
      }
    }
  }
private:
  bool _sort_spans;
};

REGISTER_KERNEL_BUILDER(Name("ExtractSpans").Device(DEVICE_CPU), ExtractSpansOp);


================================================
FILE: coref_model.py
================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os
import operator
import random
import math
import json
import threading
import numpy as np
import tensorflow as tf
import tensorflow_hub as hub
import h5py

import util
import coref_ops
import conll
import metrics

class CorefModel(object):
  def __init__(self, config):
    self.config = config
    self.context_embeddings = util.EmbeddingDictionary(config["context_embeddings"])
    self.head_embeddings = util.EmbeddingDictionary(config["head_embeddings"], maybe_cache=self.context_embeddings)
    self.char_embedding_size = config["char_embedding_size"]
    self.char_dict = util.load_char_dict(config["char_vocab_path"])
    self.max_span_width = config["max_span_width"]
    self.genres = { g:i for i,g in enumerate(config["genres"]) }
    if config["lm_path"]:
      self.lm_file = h5py.File(self.config["lm_path"], "r")
    else:
      self.lm_file = None
    self.lm_layers = self.config["lm_layers"]
    self.lm_size = self.config["lm_size"]
    self.eval_data = None # Load eval data lazily.

    input_props = []
    input_props.append((tf.string, [None, None])) # Tokens.
    input_props.append((tf.float32, [None, None, self.context_embeddings.size])) # Context embeddings.
    input_props.append((tf.float32, [None, None, self.head_embeddings.size])) # Head embeddings.
    input_props.append((tf.float32, [None, None, self.lm_size, self.lm_layers])) # LM embeddings.
    input_props.append((tf.int32, [None, None, None])) # Character indices.
    input_props.append((tf.int32, [None])) # Text lengths.
    input_props.append((tf.int32, [None])) # Speaker IDs.
    input_props.append((tf.int32, [])) # Genre.
    input_props.append((tf.bool, [])) # Is training.
    input_props.append((tf.int32, [None])) # Gold starts.
    input_props.append((tf.int32, [None])) # Gold ends.
    input_props.append((tf.int32, [None])) # Cluster ids.

    self.queue_input_tensors = [tf.placeholder(dtype, shape) for dtype, shape in input_props]
    dtypes, shapes = zip(*input_props)
    queue = tf.PaddingFIFOQueue(capacity=10, dtypes=dtypes, shapes=shapes)
    self.enqueue_op = queue.enqueue(self.queue_input_tensors)
    self.input_tensors = queue.dequeue()

    self.predictions, self.loss = self.get_predictions_and_loss(*self.input_tensors)
    self.global_step = tf.Variable(0, name="global_step", trainable=False)
    self.reset_global_step = tf.assign(self.global_step, 0)
    learning_rate = tf.train.exponential_decay(self.config["learning_rate"], self.global_step,
                                               self.config["decay_frequency"], self.config["decay_rate"], staircase=True)
    trainable_params = tf.trainable_variables()
    gradients = tf.gradients(self.loss, trainable_params)
    gradients, _ = tf.clip_by_global_norm(gradients, self.config["max_gradient_norm"])
    optimizers = {
      "adam" : tf.train.AdamOptimizer,
      "sgd" : tf.train.GradientDescentOptimizer
    }
    optimizer = optimizers[self.config["optimizer"]](learning_rate)
    self.train_op = optimizer.apply_gradients(zip(gradients, trainable_params), global_step=self.global_step)

  def start_enqueue_thread(self, session):
    with open(self.config["train_path"]) as f:
      train_examples = [json.loads(jsonline) for jsonline in f.readlines()]
    def _enqueue_loop():
      while True:
        random.shuffle(train_examples)
        for example in train_examples:
          tensorized_example = self.tensorize_example(example, is_training=True)
          feed_dict = dict(zip(self.queue_input_tensors, tensorized_example))
          session.run(self.enqueue_op, feed_dict=feed_dict)
    enqueue_thread = threading.Thread(target=_enqueue_loop)
    enqueue_thread.daemon = True
    enqueue_thread.start()

  def restore(self, session):
    # Don't try to restore unused variables from the TF-Hub ELMo module.
    vars_to_restore = [v for v in tf.global_variables() if "module/" not in v.name]
    saver = tf.train.Saver(vars_to_restore)
    checkpoint_path = os.path.join(self.config["log_dir"], "model.max.ckpt")
    print("Restoring from {}".format(checkpoint_path))
    session.run(tf.global_variables_initializer())
    saver.restore(session, checkpoint_path)

  def load_lm_embeddings(self, doc_key):
    if self.lm_file is None:
      return np.zeros([0, 0, self.lm_size, self.lm_layers])
    file_key = doc_key.replace("/", ":")
    group = self.lm_file[file_key]
    num_sentences = len(list(group.keys()))
    sentences = [group[str(i)][...] for i in range(num_sentences)]
    lm_emb = np.zeros([num_sentences, max(s.shape[0] for s in sentences), self.lm_size, self.lm_layers])
    for i, s in enumerate(sentences):
      lm_emb[i, :s.shape[0], :, :] = s
    return lm_emb

  def tensorize_mentions(self, mentions):
    if len(mentions) > 0:
      starts, ends = zip(*mentions)
    else:
      starts, ends = [], []
    return np.array(starts), np.array(ends)

  def tensorize_span_labels(self, tuples, label_dict):
    if len(tuples) > 0:
      starts, ends, labels = zip(*tuples)
    else:
      starts, ends, labels = [], [], []
    return np.array(starts), np.array(ends), np.array([label_dict[c] for c in labels])

  def tensorize_example(self, example, is_training):
    clusters = example["clusters"]

    gold_mentions = sorted(tuple(m) for m in util.flatten(clusters))
    gold_mention_map = {m:i for i,m in enumerate(gold_mentions)}
    cluster_ids = np.zeros(len(gold_mentions))
    for cluster_id, cluster in enumerate(clusters):
      for mention in cluster:
        cluster_ids[gold_mention_map[tuple(mention)]] = cluster_id + 1

    sentences = example["sentences"]
    num_words = sum(len(s) for s in sentences)
    speakers = util.flatten(example["speakers"])

    assert num_words == len(speakers)

    max_sentence_length = max(len(s) for s in sentences)
    max_word_length = max(max(max(len(w) for w in s) for s in sentences), max(self.config["filter_widths"]))
    text_len = np.array([len(s) for s in sentences])
    tokens = [[""] * max_sentence_length for _ in sentences]
    context_word_emb = np.zeros([len(sentences), max_sentence_length, self.context_embeddings.size])
    head_word_emb = np.zeros([len(sentences), max_sentence_length, self.head_embeddings.size])
    char_index = np.zeros([len(sentences), max_sentence_length, max_word_length])
    for i, sentence in enumerate(sentences):
      for j, word in enumerate(sentence):
        tokens[i][j] = word
        context_word_emb[i, j] = self.context_embeddings[word]
        head_word_emb[i, j] = self.head_embeddings[word]
        char_index[i, j, :len(word)] = [self.char_dict[c] for c in word]
    tokens = np.array(tokens)

    speaker_dict = { s:i for i,s in enumerate(set(speakers)) }
    speaker_ids = np.array([speaker_dict[s] for s in speakers])

    doc_key = example["doc_key"]
    genre = self.genres[doc_key[:2]]

    gold_starts, gold_ends = self.tensorize_mentions(gold_mentions)

    lm_emb = self.load_lm_embeddings(doc_key)

    example_tensors = (tokens, context_word_emb, head_word_emb, lm_emb, char_index, text_len, speaker_ids, genre, is_training, gold_starts, gold_ends, cluster_ids)

    if is_training and len(sentences) > self.config["max_training_sentences"]:
      return self.truncate_example(*example_tensors)
    else:
      return example_tensors

  def truncate_example(self, tokens, context_word_emb, head_word_emb, lm_emb, char_index, text_len, speaker_ids, genre, is_training, gold_starts, gold_ends, cluster_ids):
    max_training_sentences = self.config["max_training_sentences"]
    num_sentences = context_word_emb.shape[0]
    assert num_sentences > max_training_sentences

    sentence_offset = random.randint(0, num_sentences - max_training_sentences)
    word_offset = text_len[:sentence_offset].sum()
    num_words = text_len[sentence_offset:sentence_offset + max_training_sentences].sum()
    tokens = tokens[sentence_offset:sentence_offset + max_training_sentences, :]
    context_word_emb = context_word_emb[sentence_offset:sentence_offset + max_training_sentences, :, :]
    head_word_emb = head_word_emb[sentence_offset:sentence_offset + max_training_sentences, :, :]
    lm_emb = lm_emb[sentence_offset:sentence_offset + max_training_sentences, :, :, :]
    char_index = char_index[sentence_offset:sentence_offset + max_training_sentences, :, :]
    text_len = text_len[sentence_offset:sentence_offset + max_training_sentences]

    speaker_ids = speaker_ids[word_offset: word_offset + num_words]
    gold_spans = np.logical_and(gold_ends >= word_offset, gold_starts < word_offset + num_words)
    gold_starts = gold_starts[gold_spans] - word_offset
    gold_ends = gold_ends[gold_spans] - word_offset
    cluster_ids = cluster_ids[gold_spans]

    return tokens, context_word_emb, head_word_emb, lm_emb, char_index, text_len, speaker_ids, genre, is_training, gold_starts, gold_ends, cluster_ids

  def get_candidate_labels(self, candidate_starts, candidate_ends, labeled_starts, labeled_ends, labels):
    same_start = tf.equal(tf.expand_dims(labeled_starts, 1), tf.expand_dims(candidate_starts, 0)) # [num_labeled, num_candidates]
    same_end = tf.equal(tf.expand_dims(labeled_ends, 1), tf.expand_dims(candidate_ends, 0)) # [num_labeled, num_candidates]
    same_span = tf.logical_and(same_start, same_end) # [num_labeled, num_candidates]
    candidate_labels = tf.matmul(tf.expand_dims(labels, 0), tf.to_int32(same_span)) # [1, num_candidates]
    candidate_labels = tf.squeeze(candidate_labels, 0) # [num_candidates]
    return candidate_labels

  def get_dropout(self, dropout_rate, is_training):
    return 1 - (tf.to_float(is_training) * dropout_rate)

  def coarse_to_fine_pruning(self, top_span_emb, top_span_mention_scores, c):
    k = util.shape(top_span_emb, 0)
    top_span_range = tf.range(k) # [k]
    antecedent_offsets = tf.expand_dims(top_span_range, 1) - tf.expand_dims(top_span_range, 0) # [k, k]
    antecedents_mask = antecedent_offsets >= 1 # [k, k]
    fast_antecedent_scores = tf.expand_dims(top_span_mention_scores, 1) + tf.expand_dims(top_span_mention_scores, 0) # [k, k]
    fast_antecedent_scores += tf.log(tf.to_float(antecedents_mask)) # [k, k]
    fast_antecedent_scores += self.get_fast_antecedent_scores(top_span_emb) # [k, k]

    _, top_antecedents = tf.nn.top_k(fast_antecedent_scores, c, sorted=False) # [k, c]
    top_antecedents_mask = util.batch_gather(antecedents_mask, top_antecedents) # [k, c]
    top_fast_antecedent_scores = util.batch_gather(fast_antecedent_scores, top_antecedents) # [k, c]
    top_antecedent_offsets = util.batch_gather(antecedent_offsets, top_antecedents) # [k, c]
    return top_antecedents, top_antecedents_mask, top_fast_antecedent_scores, top_antecedent_offsets

  def distance_pruning(self, top_span_emb, top_span_mention_scores, c):
    k = util.shape(top_span_emb, 0)
    top_antecedent_offsets = tf.tile(tf.expand_dims(tf.range(c) + 1, 0), [k, 1]) # [k, c]
    raw_top_antecedents = tf.expand_dims(tf.range(k), 1) - top_antecedent_offsets # [k, c]
    top_antecedents_mask = raw_top_antecedents >= 0 # [k, c]
    top_antecedents = tf.maximum(raw_top_antecedents, 0) # [k, c]

    top_fast_antecedent_scores = tf.expand_dims(top_span_mention_scores, 1) + tf.gather(top_span_mention_scores, top_antecedents) # [k, c]
    top_fast_antecedent_scores += tf.log(tf.to_float(top_antecedents_mask)) # [k, c]
    return top_antecedents, top_antecedents_mask, top_fast_antecedent_scores, top_antecedent_offsets

  def get_predictions_and_loss(self, tokens, context_word_emb, head_word_emb, lm_emb, char_index, text_len, speaker_ids, genre, is_training, gold_starts, gold_ends, cluster_ids):
    self.dropout = self.get_dropout(self.config["dropout_rate"], is_training)
    self.lexical_dropout = self.get_dropout(self.config["lexical_dropout_rate"], is_training)
    self.lstm_dropout = self.get_dropout(self.config["lstm_dropout_rate"], is_training)

    num_sentences = tf.shape(context_word_emb)[0]
    max_sentence_length = tf.shape(context_word_emb)[1]

    context_emb_list = [context_word_emb]
    head_emb_list = [head_word_emb]

    if self.config["char_embedding_size"] > 0:
      char_emb = tf.gather(tf.get_variable("char_embeddings", [len(self.char_dict), self.config["char_embedding_size"]]), char_index) # [num_sentences, max_sentence_length, max_word_length, emb]
      flattened_char_emb = tf.reshape(char_emb, [num_sentences * max_sentence_length, util.shape(char_emb, 2), util.shape(char_emb, 3)]) # [num_sentences * max_sentence_length, max_word_length, emb]
      flattened_aggregated_char_emb = util.cnn(flattened_char_emb, self.config["filter_widths"], self.config["filter_size"]) # [num_sentences * max_sentence_length, emb]
      aggregated_char_emb = tf.reshape(flattened_aggregated_char_emb, [num_sentences, max_sentence_length, util.shape(flattened_aggregated_char_emb, 1)]) # [num_sentences, max_sentence_length, emb]
      context_emb_list.append(aggregated_char_emb)
      head_emb_list.append(aggregated_char_emb)

    if not self.lm_file:
      elmo_module = hub.Module("https://tfhub.dev/google/elmo/2")
      lm_embeddings = elmo_module(
          inputs={"tokens": tokens, "sequence_len": text_len},
          signature="tokens", as_dict=True)
      word_emb = lm_embeddings["word_emb"]  # [num_sentences, max_sentence_length, 512]
      lm_emb = tf.stack([tf.concat([word_emb, word_emb], -1),
                         lm_embeddings["lstm_outputs1"],
                         lm_embeddings["lstm_outputs2"]], -1)  # [num_sentences, max_sentence_length, 1024, 3]
    lm_emb_size = util.shape(lm_emb, 2)
    lm_num_layers = util.shape(lm_emb, 3)
    with tf.variable_scope("lm_aggregation"):
      self.lm_weights = tf.nn.softmax(tf.get_variable("lm_scores", [lm_num_layers], initializer=tf.constant_initializer(0.0)))
      self.lm_scaling = tf.get_variable("lm_scaling", [], initializer=tf.constant_initializer(1.0))
    flattened_lm_emb = tf.reshape(lm_emb, [num_sentences * max_sentence_length * lm_emb_size, lm_num_layers])
    flattened_aggregated_lm_emb = tf.matmul(flattened_lm_emb, tf.expand_dims(self.lm_weights, 1)) # [num_sentences * max_sentence_length * emb, 1]
    aggregated_lm_emb = tf.reshape(flattened_aggregated_lm_emb, [num_sentences, max_sentence_length, lm_emb_size])
    aggregated_lm_emb *= self.lm_scaling
    context_emb_list.append(aggregated_lm_emb)

    context_emb = tf.concat(context_emb_list, 2) # [num_sentences, max_sentence_length, emb]
    head_emb = tf.concat(head_emb_list, 2) # [num_sentences, max_sentence_length, emb]
    context_emb = tf.nn.dropout(context_emb, self.lexical_dropout) # [num_sentences, max_sentence_length, emb]
    head_emb = tf.nn.dropout(head_emb, self.lexical_dropout) # [num_sentences, max_sentence_length, emb]

    text_len_mask = tf.sequence_mask(text_len, maxlen=max_sentence_length) # [num_sentence, max_sentence_length]

    context_outputs = self.lstm_contextualize(context_emb, text_len, text_len_mask) # [num_words, emb]
    num_words = util.shape(context_outputs, 0)

    genre_emb = tf.gather(tf.get_variable("genre_embeddings", [len(self.genres), self.config["feature_size"]]), genre) # [emb]

    sentence_indices = tf.tile(tf.expand_dims(tf.range(num_sentences), 1), [1, max_sentence_length]) # [num_sentences, max_sentence_length]
    flattened_sentence_indices = self.flatten_emb_by_sentence(sentence_indices, text_len_mask) # [num_words]
    flattened_head_emb = self.flatten_emb_by_sentence(head_emb, text_len_mask) # [num_words]

    candidate_starts = tf.tile(tf.expand_dims(tf.range(num_words), 1), [1, self.max_span_width]) # [num_words, max_span_width]
    candidate_ends = candidate_starts + tf.expand_dims(tf.range(self.max_span_width), 0) # [num_words, max_span_width]
    candidate_start_sentence_indices = tf.gather(flattened_sentence_indices, candidate_starts) # [num_words, max_span_width]
    candidate_end_sentence_indices = tf.gather(flattened_sentence_indices, tf.minimum(candidate_ends, num_words - 1)) # [num_words, max_span_width]
    candidate_mask = tf.logical_and(candidate_ends < num_words, tf.equal(candidate_start_sentence_indices, candidate_end_sentence_indices)) # [num_words, max_span_width]
    flattened_candidate_mask = tf.reshape(candidate_mask, [-1]) # [num_words * max_span_width]
    candidate_starts = tf.boolean_mask(tf.reshape(candidate_starts, [-1]), flattened_candidate_mask) # [num_candidates]
    candidate_ends = tf.boolean_mask(tf.reshape(candidate_ends, [-1]), flattened_candidate_mask) # [num_candidates]
    candidate_sentence_indices = tf.boolean_mask(tf.reshape(candidate_start_sentence_indices, [-1]), flattened_candidate_mask) # [num_candidates]

    candidate_cluster_ids = self.get_candidate_labels(candidate_starts, candidate_ends, gold_starts, gold_ends, cluster_ids) # [num_candidates]

    candidate_span_emb = self.get_span_emb(flattened_head_emb, context_outputs, candidate_starts, candidate_ends) # [num_candidates, emb]
    candidate_mention_scores =  self.get_mention_scores(candidate_span_emb) # [k, 1]
    candidate_mention_scores = tf.squeeze(candidate_mention_scores, 1) # [k]

    k = tf.to_int32(tf.floor(tf.to_float(tf.shape(context_outputs)[0]) * self.config["top_span_ratio"]))
    top_span_indices = coref_ops.extract_spans(tf.expand_dims(candidate_mention_scores, 0),
                                               tf.expand_dims(candidate_starts, 0),
                                               tf.expand_dims(candidate_ends, 0),
                                               tf.expand_dims(k, 0),
                                               util.shape(context_outputs, 0),
                                               True) # [1, k]
    top_span_indices.set_shape([1, None])
    top_span_indices = tf.squeeze(top_span_indices, 0) # [k]

    top_span_starts = tf.gather(candidate_starts, top_span_indices) # [k]
    top_span_ends = tf.gather(candidate_ends, top_span_indices) # [k]
    top_span_emb = tf.gather(candidate_span_emb, top_span_indices) # [k, emb]
    top_span_cluster_ids = tf.gather(candidate_cluster_ids, top_span_indices) # [k]
    top_span_mention_scores = tf.gather(candidate_mention_scores, top_span_indices) # [k]
    top_span_sentence_indices = tf.gather(candidate_sentence_indices, top_span_indices) # [k]
    top_span_speaker_ids = tf.gather(speaker_ids, top_span_starts) # [k]

    c = tf.minimum(self.config["max_top_antecedents"], k)

    if self.config["coarse_to_fine"]:
      top_antecedents, top_antecedents_mask, top_fast_antecedent_scores, top_antecedent_offsets = self.coarse_to_fine_pruning(top_span_emb, top_span_mention_scores, c)
    else:
      top_antecedents, top_antecedents_mask, top_fast_antecedent_scores, top_antecedent_offsets = self.distance_pruning(top_span_emb, top_span_mention_scores, c)

    dummy_scores = tf.zeros([k, 1]) # [k, 1]
    for i in range(self.config["coref_depth"]):
      with tf.variable_scope("coref_layer", reuse=(i > 0)):
        top_antecedent_emb = tf.gather(top_span_emb, top_antecedents) # [k, c, emb]
        top_antecedent_scores = top_fast_antecedent_scores + self.get_slow_antecedent_scores(top_span_emb, top_antecedents, top_antecedent_emb, top_antecedent_offsets, top_span_speaker_ids, genre_emb) # [k, c]
        top_antecedent_weights = tf.nn.softmax(tf.concat([dummy_scores, top_antecedent_scores], 1)) # [k, c + 1]
        top_antecedent_emb = tf.concat([tf.expand_dims(top_span_emb, 1), top_antecedent_emb], 1) # [k, c + 1, emb]
        attended_span_emb = tf.reduce_sum(tf.expand_dims(top_antecedent_weights, 2) * top_antecedent_emb, 1) # [k, emb]
        with tf.variable_scope("f"):
          f = tf.sigmoid(util.projection(tf.concat([top_span_emb, attended_span_emb], 1), util.shape(top_span_emb, -1))) # [k, emb]
          top_span_emb = f * attended_span_emb + (1 - f) * top_span_emb # [k, emb]

    top_antecedent_scores = tf.concat([dummy_scores, top_antecedent_scores], 1) # [k, c + 1]

    top_antecedent_cluster_ids = tf.gather(top_span_cluster_ids, top_antecedents) # [k, c]
    top_antecedent_cluster_ids += tf.to_int32(tf.log(tf.to_float(top_antecedents_mask))) # [k, c]
    same_cluster_indicator = tf.equal(top_antecedent_cluster_ids, tf.expand_dims(top_span_cluster_ids, 1)) # [k, c]
    non_dummy_indicator = tf.expand_dims(top_span_cluster_ids > 0, 1) # [k, 1]
    pairwise_labels = tf.logical_and(same_cluster_indicator, non_dummy_indicator) # [k, c]
    dummy_labels = tf.logical_not(tf.reduce_any(pairwise_labels, 1, keepdims=True)) # [k, 1]
    top_antecedent_labels = tf.concat([dummy_labels, pairwise_labels], 1) # [k, c + 1]
    loss = self.softmax_loss(top_antecedent_scores, top_antecedent_labels) # [k]
    loss = tf.reduce_sum(loss) # []

    return [candidate_starts, candidate_ends, candidate_mention_scores, top_span_starts, top_span_ends, top_antecedents, top_antecedent_scores], loss

  def get_span_emb(self, head_emb, context_outputs, span_starts, span_ends):
    span_emb_list = []

    span_start_emb = tf.gather(context_outputs, span_starts) # [k, emb]
    span_emb_list.append(span_start_emb)

    span_end_emb = tf.gather(context_outputs, span_ends) # [k, emb]
    span_emb_list.append(span_end_emb)

    span_width = 1 + span_ends - span_starts # [k]

    if self.config["use_features"]:
      span_width_index = span_width - 1 # [k]
      span_width_emb = tf.gather(tf.get_variable("span_width_embeddings", [self.config["max_span_width"], self.config["feature_size"]]), span_width_index) # [k, emb]
      span_width_emb = tf.nn.dropout(span_width_emb, self.dropout)
      span_emb_list.append(span_width_emb)

    if self.config["model_heads"]:
      span_indices = tf.expand_dims(tf.range(self.config["max_span_width"]), 0) + tf.expand_dims(span_starts, 1) # [k, max_span_width]
      span_indices = tf.minimum(util.shape(context_outputs, 0) - 1, span_indices) # [k, max_span_width]
      span_text_emb = tf.gather(head_emb, span_indices) # [k, max_span_width, emb]
      with tf.variable_scope("head_scores"):
        self.head_scores = util.projection(context_outputs, 1) # [num_words, 1]
      span_head_scores = tf.gather(self.head_scores, span_indices) # [k, max_span_width, 1]
      span_mask = tf.expand_dims(tf.sequence_mask(span_width, self.config["max_span_width"], dtype=tf.float32), 2) # [k, max_span_width, 1]
      span_head_scores += tf.log(span_mask) # [k, max_span_width, 1]
      span_attention = tf.nn.softmax(span_head_scores, 1) # [k, max_span_width, 1]
      span_head_emb = tf.reduce_sum(span_attention * span_text_emb, 1) # [k, emb]
      span_emb_list.append(span_head_emb)

    span_emb = tf.concat(span_emb_list, 1) # [k, emb]
    return span_emb # [k, emb]

  def get_mention_scores(self, span_emb):
    with tf.variable_scope("mention_scores"):
      return util.ffnn(span_emb, self.config["ffnn_depth"], self.config["ffnn_size"], 1, self.dropout) # [k, 1]

  def softmax_loss(self, antecedent_scores, antecedent_labels):
    gold_scores = antecedent_scores + tf.log(tf.to_float(antecedent_labels)) # [k, max_ant + 1]
    marginalized_gold_scores = tf.reduce_logsumexp(gold_scores, [1]) # [k]
    log_norm = tf.reduce_logsumexp(antecedent_scores, [1]) # [k]
    return log_norm - marginalized_gold_scores # [k]

  def bucket_distance(self, distances):
    """
    Places the given values (designed for distances) into 10 semi-logscale buckets:
    [0, 1, 2, 3, 4, 5-7, 8-15, 16-31, 32-63, 64+].
    """
    logspace_idx = tf.to_int32(tf.floor(tf.log(tf.to_float(distances))/math.log(2))) + 3
    use_identity = tf.to_int32(distances <= 4)
    combined_idx = use_identity * distances + (1 - use_identity) * logspace_idx
    return tf.clip_by_value(combined_idx, 0, 9)

  def get_slow_antecedent_scores(self, top_span_emb, top_antecedents, top_antecedent_emb, top_antecedent_offsets, top_span_speaker_ids, genre_emb):
    k = util.shape(top_span_emb, 0)
    c = util.shape(top_antecedents, 1)

    feature_emb_list = []

    if self.config["use_metadata"]:
      top_antecedent_speaker_ids = tf.gather(top_span_speaker_ids, top_antecedents) # [k, c]
      same_speaker = tf.equal(tf.expand_dims(top_span_speaker_ids, 1), top_antecedent_speaker_ids) # [k, c]
      speaker_pair_emb = tf.gather(tf.get_variable("same_speaker_emb", [2, self.config["feature_size"]]), tf.to_int32(same_speaker)) # [k, c, emb]
      feature_emb_list.append(speaker_pair_emb)

      tiled_genre_emb = tf.tile(tf.expand_dims(tf.expand_dims(genre_emb, 0), 0), [k, c, 1]) # [k, c, emb]
      feature_emb_list.append(tiled_genre_emb)

    if self.config["use_features"]:
      antecedent_distance_buckets = self.bucket_distance(top_antecedent_offsets) # [k, c]
      antecedent_distance_emb = tf.gather(tf.get_variable("antecedent_distance_emb", [10, self.config["feature_size"]]), antecedent_distance_buckets) # [k, c]
      feature_emb_list.append(antecedent_distance_emb)

    feature_emb = tf.concat(feature_emb_list, 2) # [k, c, emb]
    feature_emb = tf.nn.dropout(feature_emb, self.dropout) # [k, c, emb]

    target_emb = tf.expand_dims(top_span_emb, 1) # [k, 1, emb]
    similarity_emb = top_antecedent_emb * target_emb # [k, c, emb]
    target_emb = tf.tile(target_emb, [1, c, 1]) # [k, c, emb]

    pair_emb = tf.concat([target_emb, top_antecedent_emb, similarity_emb, feature_emb], 2) # [k, c, emb]

    with tf.variable_scope("slow_antecedent_scores"):
      slow_antecedent_scores = util.ffnn(pair_emb, self.config["ffnn_depth"], self.config["ffnn_size"], 1, self.dropout) # [k, c, 1]
    slow_antecedent_scores = tf.squeeze(slow_antecedent_scores, 2) # [k, c]
    return slow_antecedent_scores # [k, c]

  def get_fast_antecedent_scores(self, top_span_emb):
    with tf.variable_scope("src_projection"):
      source_top_span_emb = tf.nn.dropout(util.projection(top_span_emb, util.shape(top_span_emb, -1)), self.dropout) # [k, emb]
    target_top_span_emb = tf.nn.dropout(top_span_emb, self.dropout) # [k, emb]
    return tf.matmul(source_top_span_emb, target_top_span_emb, transpose_b=True) # [k, k]

  def flatten_emb_by_sentence(self, emb, text_len_mask):
    num_sentences = tf.shape(emb)[0]
    max_sentence_length = tf.shape(emb)[1]

    emb_rank = len(emb.get_shape())
    if emb_rank  == 2:
      flattened_emb = tf.reshape(emb, [num_sentences * max_sentence_length])
    elif emb_rank == 3:
      flattened_emb = tf.reshape(emb, [num_sentences * max_sentence_length, util.shape(emb, 2)])
    else:
      raise ValueError("Unsupported rank: {}".format(emb_rank))
    return tf.boolean_mask(flattened_emb, tf.reshape(text_len_mask, [num_sentences * max_sentence_length]))

  def lstm_contextualize(self, text_emb, text_len, text_len_mask):
    num_sentences = tf.shape(text_emb)[0]

    current_inputs = text_emb # [num_sentences, max_sentence_length, emb]

    for layer in range(self.config["contextualization_layers"]):
      with tf.variable_scope("layer_{}".format(layer)):
        with tf.variable_scope("fw_cell"):
          cell_fw = util.CustomLSTMCell(self.config["contextualization_size"], num_sentences, self.lstm_dropout)
        with tf.variable_scope("bw_cell"):
          cell_bw = util.CustomLSTMCell(self.config["contextualization_size"], num_sentences, self.lstm_dropout)
        state_fw = tf.contrib.rnn.LSTMStateTuple(tf.tile(cell_fw.initial_state.c, [num_sentences, 1]), tf.tile(cell_fw.initial_state.h, [num_sentences, 1]))
        state_bw = tf.contrib.rnn.LSTMStateTuple(tf.tile(cell_bw.initial_state.c, [num_sentences, 1]), tf.tile(cell_bw.initial_state.h, [num_sentences, 1]))

        (fw_outputs, bw_outputs), _ = tf.nn.bidirectional_dynamic_rnn(
          cell_fw=cell_fw,
          cell_bw=cell_bw,
          inputs=current_inputs,
          sequence_length=text_len,
          initial_state_fw=state_fw,
          initial_state_bw=state_bw)

        text_outputs = tf.concat([fw_outputs, bw_outputs], 2) # [num_sentences, max_sentence_length, emb]
        text_outputs = tf.nn.dropout(text_outputs, self.lstm_dropout)
        if layer > 0:
          highway_gates = tf.sigmoid(util.projection(text_outputs, util.shape(text_outputs, 2))) # [num_sentences, max_sentence_length, emb]
          text_outputs = highway_gates * text_outputs + (1 - highway_gates) * current_inputs
        current_inputs = text_outputs

    return self.flatten_emb_by_sentence(text_outputs, text_len_mask)

  def get_predicted_antecedents(self, antecedents, antecedent_scores):
    predicted_antecedents = []
    for i, index in enumerate(np.argmax(antecedent_scores, axis=1) - 1):
      if index < 0:
        predicted_antecedents.append(-1)
      else:
        predicted_antecedents.append(antecedents[i, index])
    return predicted_antecedents

  def get_predicted_clusters(self, top_span_starts, top_span_ends, predicted_antecedents):
    mention_to_predicted = {}
    predicted_clusters = []
    for i, predicted_index in enumerate(predicted_antecedents):
      if predicted_index < 0:
        continue
      assert i > predicted_index
      predicted_antecedent = (int(top_span_starts[predicted_index]), int(top_span_ends[predicted_index]))
      if predicted_antecedent in mention_to_predicted:
        predicted_cluster = mention_to_predicted[predicted_antecedent]
      else:
        predicted_cluster = len(predicted_clusters)
        predicted_clusters.append([predicted_antecedent])
        mention_to_predicted[predicted_antecedent] = predicted_cluster

      mention = (int(top_span_starts[i]), int(top_span_ends[i]))
      predicted_clusters[predicted_cluster].append(mention)
      mention_to_predicted[mention] = predicted_cluster

    predicted_clusters = [tuple(pc) for pc in predicted_clusters]
    mention_to_predicted = { m:predicted_clusters[i] for m,i in mention_to_predicted.items() }

    return predicted_clusters, mention_to_predicted

  def evaluate_coref(self, top_span_starts, top_span_ends, predicted_antecedents, gold_clusters, evaluator):
    gold_clusters = [tuple(tuple(m) for m in gc) for gc in gold_clusters]
    mention_to_gold = {}
    for gc in gold_clusters:
      for mention in gc:
        mention_to_gold[mention] = gc

    predicted_clusters, mention_to_predicted = self.get_predicted_clusters(top_span_starts, top_span_ends, predicted_antecedents)
    evaluator.update(predicted_clusters, gold_clusters, mention_to_predicted, mention_to_gold)
    return predicted_clusters

  def load_eval_data(self):
    if self.eval_data is None:
      def load_line(line):
        example = json.loads(line)
        return self.tensorize_example(example, is_training=False), example
      with open(self.config["eval_path"]) as f:
        self.eval_data = [load_line(l) for l in f.readlines()]
      num_words = sum(tensorized_example[2].sum() for tensorized_example, _ in self.eval_data)
      print("Loaded {} eval examples.".format(len(self.eval_data)))

  def evaluate(self, session, official_stdout=False):
    self.load_eval_data()

    coref_predictions = {}
    coref_evaluator = metrics.CorefEvaluator()

    for example_num, (tensorized_example, example) in enumerate(self.eval_data):
      _, _, _, _, _, _, _, _, _, gold_starts, gold_ends, _ = tensorized_example
      feed_dict = {i:t for i,t in zip(self.input_tensors, tensorized_example)}
      candidate_starts, candidate_ends, candidate_mention_scores, top_span_starts, top_span_ends, top_antecedents, top_antecedent_scores = session.run(self.predictions, feed_dict=feed_dict)
      predicted_antecedents = self.get_predicted_antecedents(top_antecedents, top_antecedent_scores)
      coref_predictions[example["doc_key"]] = self.evaluate_coref(top_span_starts, top_span_ends, predicted_antecedents, example["clusters"], coref_evaluator)
      if example_num % 10 == 0:
        print("Evaluated {}/{} examples.".format(example_num + 1, len(self.eval_data)))

    summary_dict = {}
    conll_results = conll.evaluate_conll(self.config["conll_eval_path"], coref_predictions, official_stdout)
    average_f1 = sum(results["f"] for results in conll_results.values()) / len(conll_results)
    summary_dict["Average F1 (conll)"] = average_f1
    print("Average F1 (conll): {:.2f}%".format(average_f1))

    p,r,f = coref_evaluator.get_prf()
    summary_dict["Average F1 (py)"] = f
    print("Average F1 (py): {:.2f}%".format(f * 100))
    summary_dict["Average precision (py)"] = p
    print("Average precision (py): {:.2f}%".format(p * 100))
    summary_dict["Average recall (py)"] = r
    print("Average recall (py): {:.2f}%".format(r * 100))

    return util.make_summary(summary_dict), average_f1


================================================
FILE: coref_ops.py
================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow as tf
from tensorflow.python import pywrap_tensorflow

coref_op_library = tf.load_op_library("./coref_kernels.so")

extract_spans = coref_op_library.extract_spans
tf.NotDifferentiable("ExtractSpans")


================================================
FILE: demo.py
================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from six.moves import input
import tensorflow as tf
import coref_model as cm
import util

import nltk
nltk.download("punkt")
from nltk.tokenize import sent_tokenize, word_tokenize

def create_example(text):
  raw_sentences = sent_tokenize(text)
  sentences = [word_tokenize(s) for s in raw_sentences]
  speakers = [["" for _ in sentence] for sentence in sentences]
  return {
    "doc_key": "nw",
    "clusters": [],
    "sentences": sentences,
    "speakers": speakers,
  }

def print_predictions(example):
  words = util.flatten(example["sentences"])
  for cluster in example["predicted_clusters"]:
    print(u"Predicted cluster: {}".format([" ".join(words[m[0]:m[1]+1]) for m in cluster]))

def make_predictions(text, model):
  example = create_example(text)
  tensorized_example = model.tensorize_example(example, is_training=False)
  feed_dict = {i:t for i,t in zip(model.input_tensors, tensorized_example)}
  _, _, _, mention_starts, mention_ends, antecedents, antecedent_scores, head_scores = session.run(model.predictions + [model.head_scores], feed_dict=feed_dict)

  predicted_antecedents = model.get_predicted_antecedents(antecedents, antecedent_scores)

  example["predicted_clusters"], _ = model.get_predicted_clusters(mention_starts, mention_ends, predicted_antecedents)
  example["top_spans"] = zip((int(i) for i in mention_starts), (int(i) for i in mention_ends))
  example["head_scores"] = head_scores.tolist()
  return example

if __name__ == "__main__":
  config = util.initialize_from_env()
  model = cm.CorefModel(config)
  with tf.Session() as session:
    model.restore(session)
    while True:
      text = input("Document text: ")
      if len(text) > 0:
        print_predictions(make_predictions(text, model))


================================================
FILE: evaluate.py
================================================
#!/usr/bin/env python
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os

import tensorflow as tf
import coref_model as cm
import util

if __name__ == "__main__":
  config = util.initialize_from_env()
  model = cm.CorefModel(config)
  with tf.Session() as session:
    model.restore(session)
    model.evaluate(session, official_stdout=True)


================================================
FILE: experiments.conf
================================================
# Word embeddings.
glove_300d {
  path = glove.840B.300d.txt
  size = 300
}
glove_300d_filtered {
  path = glove.840B.300d.txt.filtered
  size = 300
}
glove_300d_2w {
  path = glove_50_300_2.txt
  size = 300
}

# Distributed training configurations.
two_local_gpus {
  addresses {
    ps = [localhost:2222]
    worker = [localhost:2223, localhost:2224]
  }
  gpus = [0, 1]
}

# Main configuration.
best {
  # Computation limits.
  max_top_antecedents = 50
  max_training_sentences = 50
  top_span_ratio = 0.4

  # Model hyperparameters.
  filter_widths = [3, 4, 5]
  filter_size = 50
  char_embedding_size = 8
  char_vocab_path = "char_vocab.english.txt"
  context_embeddings = ${glove_300d_filtered}
  head_embeddings = ${glove_300d_2w}
  contextualization_size = 200
  contextualization_layers = 3
  ffnn_size = 150
  ffnn_depth = 2
  feature_size = 20
  max_span_width = 30
  use_metadata = true
  use_features = true
  model_heads = true
  coref_depth = 2
  lm_layers = 3
  lm_size = 1024
  coarse_to_fine = true

  # Learning hyperparameters.
  max_gradient_norm = 5.0
  lstm_dropout_rate = 0.4
  lexical_dropout_rate = 0.5
  dropout_rate = 0.2
  optimizer = adam
  learning_rate = 0.001
  decay_rate = 0.999
  decay_frequency = 100

  # Other.
  train_path = train.english.jsonlines
  eval_path = dev.english.jsonlines
  conll_eval_path = dev.english.v4_gold_conll
  lm_path = elmo_cache.hdf5
  genres = ["bc", "bn", "mz", "nw", "pt", "tc", "wb"]
  eval_frequency = 5000
  report_frequency = 100
  log_root = logs
  cluster = ${two_local_gpus}
}

# For evaluation. Do not use for training (i.e. only for predict.py, evaluate.py, and demo.py). Rename `best` directory to `final`.
final = ${best} {
  context_embeddings = ${glove_300d}
  head_embeddings = ${glove_300d_2w}
  lm_path = ""
  eval_path = test.english.jsonlines
  conll_eval_path = test.english.v4_gold_conll
}

# Baselines.
c2f_100_ant = ${best} {
  max_top_antecedents = 100
}
c2f_250_ant = ${best} {
  max_top_antecedents = 250
}
c2f_1_layer = ${best} {
  coref_depth = 1
}
c2f_3_layer = ${best} {
  coref_depth = 3
}
distance_50_ant = ${best} {
  max_top_antecedents = 50
  coarse_to_fine = false
  coref_depth = 1
}
distance_100_ant = ${distance_50_ant} {
  max_top_antecedents = 100
}
distance_250_ant = ${distance_50_ant} {
  max_top_antecedents = 250
}


================================================
FILE: filter_embeddings.py
================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import sys
import json

if __name__ == "__main__":
  if len(sys.argv) < 3:
    sys.exit("Usage: {} <embeddings> <json1> <json2> ...".format(sys.argv[0]))

  words_to_keep = set()
  for json_filename in sys.argv[2:]:
    with open(json_filename) as json_file:
      for line in json_file.readlines():
        for sentence in json.loads(line)["sentences"]:
          words_to_keep.update(sentence)

  print("Found {} words in {} dataset(s).".format(len(words_to_keep), len(sys.argv) - 2))

  total_lines = 0
  kept_lines = 0
  out_filename = "{}.filtered".format(sys.argv[1])
  with open(sys.argv[1]) as in_file:
    with open(out_filename, "w") as out_file:
      for line in in_file.readlines():
        total_lines += 1
        word = line.split()[0]
        if word in words_to_keep:
          kept_lines += 1
          out_file.write(line)

  print("Kept {} out of {} lines.".format(kept_lines, total_lines))
  print("Wrote result to {}.".format(out_filename))


================================================
FILE: get_char_vocab.py
================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import sys
import json
import io

def get_char_vocab(input_filenames, output_filename):
  vocab = set()
  for filename in input_filenames:
    with open(filename) as f:
      for line in f.readlines():
        for sentence in json.loads(line)["sentences"]:
          for word in sentence:
            vocab.update(word)
  vocab = sorted(list(vocab))
  with io.open(output_filename, mode="w", encoding="utf8") as f:
    for char in vocab:
      f.write(char)
      f.write(u"\n")
  print("Wrote {} characters to {}".format(len(vocab), output_filename))

def get_char_vocab_language(language):
  get_char_vocab(["{}.{}.jsonlines".format(partition, language) for partition in ("train", "dev", "test")], "char_vocab.{}.txt".format(language))

get_char_vocab_language("english")
get_char_vocab_language("chinese")
get_char_vocab_language("arabic")


================================================
FILE: metrics.py
================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import numpy as np
from collections import Counter
from sklearn.utils.linear_assignment_ import linear_assignment


def f1(p_num, p_den, r_num, r_den, beta=1):
    p = 0 if p_den == 0 else p_num / float(p_den)
    r = 0 if r_den == 0 else r_num / float(r_den)
    return 0 if p + r == 0 else (1 + beta * beta) * p * r / (beta * beta * p + r)

class CorefEvaluator(object):
    def __init__(self):
        self.evaluators = [Evaluator(m) for m in (muc, b_cubed, ceafe)]

    def update(self, predicted, gold, mention_to_predicted, mention_to_gold):
        for e in self.evaluators:
            e.update(predicted, gold, mention_to_predicted, mention_to_gold)

    def get_f1(self):
        return sum(e.get_f1() for e in self.evaluators) / len(self.evaluators)

    def get_recall(self):
        return sum(e.get_recall() for e in self.evaluators) / len(self.evaluators)

    def get_precision(self):
        return sum(e.get_precision() for e in self.evaluators) / len(self.evaluators)

    def get_prf(self):
        return self.get_precision(), self.get_recall(), self.get_f1()

class Evaluator(object):
    def __init__(self, metric, beta=1):
        self.p_num = 0
        self.p_den = 0
        self.r_num = 0
        self.r_den = 0
        self.metric = metric
        self.beta = beta

    def update(self, predicted, gold, mention_to_predicted, mention_to_gold):
        if self.metric == ceafe:
            pn, pd, rn, rd = self.metric(predicted, gold)
        else:
            pn, pd = self.metric(predicted, mention_to_gold)
            rn, rd = self.metric(gold, mention_to_predicted)
        self.p_num += pn
        self.p_den += pd
        self.r_num += rn
        self.r_den += rd

    def get_f1(self):
        return f1(self.p_num, self.p_den, self.r_num, self.r_den, beta=self.beta)

    def get_recall(self):
        return 0 if self.r_num == 0 else self.r_num / float(self.r_den)

    def get_precision(self):
        return 0 if self.p_num == 0 else self.p_num / float(self.p_den)

    def get_prf(self):
        return self.get_precision(), self.get_recall(), self.get_f1()

    def get_counts(self):
        return self.p_num, self.p_den, self.r_num, self.r_den


def evaluate_documents(documents, metric, beta=1):
    evaluator = Evaluator(metric, beta=beta)
    for document in documents:
        evaluator.update(document)
    return evaluator.get_precision(), evaluator.get_recall(), evaluator.get_f1()


def b_cubed(clusters, mention_to_gold):
    num, dem = 0, 0

    for c in clusters:
        if len(c) == 1:
            continue

        gold_counts = Counter()
        correct = 0
        for m in c:
            if m in mention_to_gold:
                gold_counts[tuple(mention_to_gold[m])] += 1
        for c2, count in gold_counts.items():
            if len(c2) != 1:
                correct += count * count

        num += correct / float(len(c))
        dem += len(c)

    return num, dem


def muc(clusters, mention_to_gold):
    tp, p = 0, 0
    for c in clusters:
        p += len(c) - 1
        tp += len(c)
        linked = set()
        for m in c:
            if m in mention_to_gold:
                linked.add(mention_to_gold[m])
            else:
                tp -= 1
        tp -= len(linked)
    return tp, p


def phi4(c1, c2):
    return 2 * len([m for m in c1 if m in c2]) / float(len(c1) + len(c2))


def ceafe(clusters, gold_clusters):
    clusters = [c for c in clusters if len(c) != 1]
    scores = np.zeros((len(gold_clusters), len(clusters)))
    for i in range(len(gold_clusters)):
        for j in range(len(clusters)):
            scores[i, j] = phi4(gold_clusters[i], clusters[j])
    matching = linear_assignment(-scores)
    similarity = sum(scores[matching[:, 0], matching[:, 1]])
    return similarity, len(clusters), similarity, len(gold_clusters)


def lea(clusters, mention_to_gold):
    num, dem = 0, 0

    for c in clusters:
        if len(c) == 1:
            continue

        common_links = 0
        all_links = len(c) * (len(c) - 1) / 2.0
        for i, m in enumerate(c):
            if m in mention_to_gold:
                for m2 in c[i + 1:]:
                    if m2 in mention_to_gold and mention_to_gold[m] == mention_to_gold[m2]:
                        common_links += 1

        num += len(c) * common_links / float(all_links)
        dem += len(c)

    return num, dem


================================================
FILE: minimize.py
================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import re
import os
import sys
import json
import tempfile
import subprocess
import collections

import util
import conll

class DocumentState(object):
  def __init__(self):
    self.doc_key = None
    self.text = []
    self.text_speakers = []
    self.speakers = []
    self.sentences = []
    self.constituents = {}
    self.const_stack = []
    self.ner = {}
    self.ner_stack = []
    self.clusters = collections.defaultdict(list)
    self.coref_stacks = collections.defaultdict(list)

  def assert_empty(self):
    assert self.doc_key is None
    assert len(self.text) == 0
    assert len(self.text_speakers) == 0
    assert len(self.speakers) == 0
    assert len(self.sentences) == 0
    assert len(self.constituents) == 0
    assert len(self.const_stack) == 0
    assert len(self.ner) == 0
    assert len(self.ner_stack) == 0
    assert len(self.coref_stacks) == 0
    assert len(self.clusters) == 0

  def assert_finalizable(self):
    assert self.doc_key is not None
    assert len(self.text) == 0
    assert len(self.text_speakers) == 0
    assert len(self.speakers) > 0
    assert len(self.sentences) > 0
    assert len(self.constituents) > 0
    assert len(self.const_stack) == 0
    assert len(self.ner_stack) == 0
    assert all(len(s) == 0 for s in self.coref_stacks.values())

  def span_dict_to_list(self, span_dict):
    return [(s,e,l) for (s,e),l in span_dict.items()]

  def finalize(self):
    merged_clusters = []
    for c1 in self.clusters.values():
      existing = None
      for m in c1:
        for c2 in merged_clusters:
          if m in c2:
            existing = c2
            break
        if existing is not None:
          break
      if existing is not None:
        print("Merging clusters (shouldn't happen very often.)")
        existing.update(c1)
      else:
        merged_clusters.append(set(c1))
    merged_clusters = [list(c) for c in merged_clusters]
    all_mentions = util.flatten(merged_clusters)
    assert len(all_mentions) == len(set(all_mentions))

    return {
      "doc_key": self.doc_key,
      "sentences": self.sentences,
      "speakers": self.speakers,
      "constituents": self.span_dict_to_list(self.constituents),
      "ner": self.span_dict_to_list(self.ner),
      "clusters": merged_clusters
    }

def normalize_word(word, language):
  if language == "arabic":
    word = word[:word.find("#")]
  if word == "/." or word == "/?":
    return word[1:]
  else:
    return word

def handle_bit(word_index, bit, stack, spans):
  asterisk_idx = bit.find("*")
  if asterisk_idx >= 0:
    open_parens = bit[:asterisk_idx]
    close_parens = bit[asterisk_idx + 1:]
  else:
    open_parens = bit[:-1]
    close_parens = bit[-1]

  current_idx = open_parens.find("(")
  while current_idx >= 0:
    next_idx = open_parens.find("(", current_idx + 1)
    if next_idx >= 0:
      label = open_parens[current_idx + 1:next_idx]
    else:
      label = open_parens[current_idx + 1:]
    stack.append((word_index, label))
    current_idx = next_idx

  for c in close_parens:
    assert c == ")"
    open_index, label = stack.pop()
    current_span = (open_index, word_index)
    """
    if current_span in spans:
      spans[current_span] += "_" + label
    else:
      spans[current_span] = label
    """
    spans[current_span] = label

def handle_line(line, document_state, language, labels, stats):
  begin_document_match = re.match(conll.BEGIN_DOCUMENT_REGEX, line)
  if begin_document_match:
    document_state.assert_empty()
    document_state.doc_key = conll.get_doc_key(begin_document_match.group(1), begin_document_match.group(2))
    return None
  elif line.startswith("#end document"):
    document_state.assert_finalizable()
    finalized_state = document_state.finalize()
    stats["num_clusters"] += len(finalized_state["clusters"])
    stats["num_mentions"] += sum(len(c) for c in finalized_state["clusters"])
    labels["{}_const_labels".format(language)].update(l for _, _, l in finalized_state["constituents"])
    labels["ner"].update(l for _, _, l in finalized_state["ner"])
    return finalized_state
  else:
    row = line.split()
    if len(row) == 0:
      stats["max_sent_len_{}".format(language)] = max(len(document_state.text), stats["max_sent_len_{}".format(language)])
      stats["num_sents_{}".format(language)] += 1
      document_state.sentences.append(tuple(document_state.text))
      del document_state.text[:]
      document_state.speakers.append(tuple(document_state.text_speakers))
      del document_state.text_speakers[:]
      return None
    assert len(row) >= 12

    doc_key = conll.get_doc_key(row[0], row[1])
    word = normalize_word(row[3], language)
    parse = row[5]
    speaker = row[9]
    ner = row[10]
    coref = row[-1]

    word_index = len(document_state.text) + sum(len(s) for s in document_state.sentences)
    document_state.text.append(word)
    document_state.text_speakers.append(speaker)

    handle_bit(word_index, parse, document_state.const_stack, document_state.constituents)
    handle_bit(word_index, ner, document_state.ner_stack, document_state.ner)

    if coref != "-":
      for segment in coref.split("|"):
        if segment[0] == "(":
          if segment[-1] == ")":
            cluster_id = int(segment[1:-1])
            document_state.clusters[cluster_id].append((word_index, word_index))
          else:
            cluster_id = int(segment[1:])
            document_state.coref_stacks[cluster_id].append(word_index)
        else:
          cluster_id = int(segment[:-1])
          start = document_state.coref_stacks[cluster_id].pop()
          document_state.clusters[cluster_id].append((start, word_index))
    return None

def minimize_partition(name, language, extension, labels, stats):
  input_path = "{}.{}.{}".format(name, language, extension)
  output_path = "{}.{}.jsonlines".format(name, language)
  count = 0
  print("Minimizing {}".format(input_path))
  with open(input_path, "r") as input_file:
    with open(output_path, "w") as output_file:
      document_state = DocumentState()
      for line in input_file.readlines():
        document = handle_line(line, document_state, language, labels, stats)
        if document is not None:
          output_file.write(json.dumps(document))
          output_file.write("\n")
          count += 1
          document_state = DocumentState()
  print("Wrote {} documents to {}".format(count, output_path))

def minimize_language(language, labels, stats):
  minimize_partition("dev", language, "v4_gold_conll", labels, stats)
  minimize_partition("train", language, "v4_gold_conll", labels, stats)
  minimize_partition("test", language, "v4_gold_conll", labels, stats)

if __name__ == "__main__":
  labels = collections.defaultdict(set)
  stats = collections.defaultdict(int)
  minimize_language("english", labels, stats)
  minimize_language("chinese", labels, stats)
  minimize_language("arabic", labels, stats)
  for k, v in labels.items():
    print("{} = [{}]".format(k, ", ".join("\"{}\"".format(label) for label in v)))
  for k, v in stats.items():
    print("{} = {}".format(k, v))


================================================
FILE: predict.py
================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import sys
import json

import tensorflow as tf
import coref_model as cm
import util

if __name__ == "__main__":
  config = util.initialize_from_env()

  # Input file in .jsonlines format.
  input_filename = sys.argv[2]

  # Predictions will be written to this file in .jsonlines format.
  output_filename = sys.argv[3]

  model = cm.CorefModel(config)

  with tf.Session() as session:
    model.restore(session)

    with open(output_filename, "w") as output_file:
      with open(input_filename) as input_file:
        for example_num, line in enumerate(input_file.readlines()):
          example = json.loads(line)
          tensorized_example = model.tensorize_example(example, is_training=False)
          feed_dict = {i:t for i,t in zip(model.input_tensors, tensorized_example)}
          _, _, _, top_span_starts, top_span_ends, top_antecedents, top_antecedent_scores = session.run(model.predictions, feed_dict=feed_dict)
          predicted_antecedents = model.get_predicted_antecedents(top_antecedents, top_antecedent_scores)
          example["predicted_clusters"], _ = model.get_predicted_clusters(top_span_starts, top_span_ends, predicted_antecedents)

          output_file.write(json.dumps(example))
          output_file.write("\n")
          if example_num % 100 == 0:
            print("Decoded {} examples.".format(example_num + 1))


================================================
FILE: ps.py
================================================
#!/usr/bin/env python

import os

import tensorflow as tf
import util

if __name__ == "__main__":
  config = util.initialize_from_env()
  report_frequency = config["report_frequency"]
  cluster_config = config["cluster"]
  util.set_gpus()
  cluster = tf.train.ClusterSpec(cluster_config["addresses"])
  server = tf.train.Server(cluster, job_name="ps", task_index=0)
  server.join()


================================================
FILE: requirements.txt
================================================
tensorflow-gpu>=1.13.1
tensorflow-hub>=0.4.0
h5py
nltk
pyhocon
scipy
sklearn


================================================
FILE: setup_all.sh
================================================
#!/bin/bash

# Download pretrained embeddings.
curl -O http://downloads.cs.stanford.edu/nlp/data/glove.840B.300d.zip
unzip glove.840B.300d.zip
rm glove.840B.300d.zip

# Build custom kernels.
TF_CFLAGS=( $(python -c 'import tensorflow as tf; print(" ".join(tf.sysconfig.get_compile_flags()))') )
TF_LFLAGS=( $(python -c 'import tensorflow as tf; print(" ".join(tf.sysconfig.get_link_flags()))') )

# Linux (pip)
g++ -std=c++11 -shared coref_kernels.cc -o coref_kernels.so -fPIC ${TF_CFLAGS[@]} ${TF_LFLAGS[@]} -O2 -D_GLIBCXX_USE_CXX11_ABI=0

# Linux (build from source)
#g++ -std=c++11 -shared coref_kernels.cc -o coref_kernels.so -fPIC ${TF_CFLAGS[@]} ${TF_LFLAGS[@]} -O2

# Mac
#g++ -std=c++11 -shared coref_kernels.cc -o coref_kernels.so -I -fPIC ${TF_CFLAGS[@]} ${TF_LFLAGS[@]} -O2 -D_GLIBCXX_USE_CXX11_ABI=0 -undefined dynamic_lookup


================================================
FILE: setup_training.sh
================================================
#!/bin/bash

dlx() {
  wget $1/$2
  tar -xvzf $2
  rm $2
}

conll_url=http://conll.cemantix.org/2012/download
dlx $conll_url conll-2012-train.v4.tar.gz
dlx $conll_url conll-2012-development.v4.tar.gz
dlx $conll_url/test conll-2012-test-key.tar.gz
dlx $conll_url/test conll-2012-test-official.v9.tar.gz

dlx $conll_url conll-2012-scripts.v3.tar.gz

dlx http://conll.cemantix.org/download reference-coreference-scorers.v8.01.tar.gz
mv reference-coreference-scorers conll-2012/scorer

ontonotes_path=/projects/WebWare6/ontonotes-release-5.0
bash conll-2012/v3/scripts/skeleton2conll.sh -D $ontonotes_path/data/files/data conll-2012

function compile_partition() {
    rm -f $2.$5.$3$4
    cat conll-2012/$3/data/$1/data/$5/annotations/*/*/*/*.$3$4 >> $2.$5.$3$4
}

function compile_language() {
    compile_partition development dev v4 _gold_conll $1
    compile_partition train train v4 _gold_conll $1
    compile_partition test test v4 _gold_conll $1
}

compile_language english
compile_language chinese
compile_language arabic

python minimize.py
python get_char_vocab.py

python filter_embeddings.py glove.840B.300d.txt train.english.jsonlines dev.english.jsonlines
python cache_elmo.py train.english.jsonlines dev.english.jsonlines


================================================
FILE: train.py
================================================
#!/usr/bin/env python
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os
import time

import tensorflow as tf
import coref_model as cm
import util

if __name__ == "__main__":
  config = util.initialize_from_env()

  report_frequency = config["report_frequency"]
  eval_frequency = config["eval_frequency"]

  model = cm.CorefModel(config)
  saver = tf.train.Saver()

  log_dir = config["log_dir"]
  writer = tf.summary.FileWriter(log_dir, flush_secs=20)

  max_f1 = 0

  with tf.Session() as session:
    session.run(tf.global_variables_initializer())
    model.start_enqueue_thread(session)
    accumulated_loss = 0.0

    ckpt = tf.train.get_checkpoint_state(log_dir)
    if ckpt and ckpt.model_checkpoint_path:
      print("Restoring from: {}".format(ckpt.model_checkpoint_path))
      saver.restore(session, ckpt.model_checkpoint_path)

    initial_time = time.time()
    while True:
      tf_loss, tf_global_step, _ = session.run([model.loss, model.global_step, model.train_op])
      accumulated_loss += tf_loss

      if tf_global_step % report_frequency == 0:
        total_time = time.time() - initial_time
        steps_per_second = tf_global_step / total_time

        average_loss = accumulated_loss / report_frequency
        print("[{}] loss={:.2f}, steps/s={:.2f}".format(tf_global_step, average_loss, steps_per_second))
        writer.add_summary(util.make_summary({"loss": average_loss}), tf_global_step)
        accumulated_loss = 0.0

      if tf_global_step % eval_frequency == 0:
        saver.save(session, os.path.join(log_dir, "model"), global_step=tf_global_step)
        eval_summary, eval_f1 = model.evaluate(session)

        if eval_f1 > max_f1:
          max_f1 = eval_f1
          util.copy_checkpoint(os.path.join(log_dir, "model-{}".format(tf_global_step)), os.path.join(log_dir, "model.max.ckpt"))

        writer.add_summary(eval_summary, tf_global_step)
        writer.add_summary(util.make_summary({"max_eval_f1": max_f1}), tf_global_step)

        print("[{}] evaL_f1={:.2f}, max_f1={:.2f}".format(tf_global_step, eval_f1, max_f1))


================================================
FILE: util.py
================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os
import errno
import codecs
import collections
import json
import math
import shutil
import sys

import numpy as np
import tensorflow as tf
import pyhocon


def initialize_from_env():
  if "GPU" in os.environ:
    set_gpus(int(os.environ["GPU"]))
  else:
    set_gpus()

  name = sys.argv[1]
  print("Running experiment: {}".format(name))

  config = pyhocon.ConfigFactory.parse_file("experiments.conf")[name]
  config["log_dir"] = mkdirs(os.path.join(config["log_root"], name))

  print(pyhocon.HOCONConverter.convert(config, "hocon"))
  return config

def copy_checkpoint(source, target):
  for ext in (".index", ".data-00000-of-00001"):
    shutil.copyfile(source + ext, target + ext)

def make_summary(value_dict):
  return tf.Summary(value=[tf.Summary.Value(tag=k, simple_value=v) for k,v in value_dict.items()])

def flatten(l):
  return [item for sublist in l for item in sublist]

def set_gpus(*gpus):
  os.environ["CUDA_VISIBLE_DEVICES"] = ",".join(str(g) for g in gpus)
  print("Setting CUDA_VISIBLE_DEVICES to: {}".format(os.environ["CUDA_VISIBLE_DEVICES"]))

def mkdirs(path):
  try:
    os.makedirs(path)
  except OSError as exception:
    if exception.errno != errno.EEXIST:
      raise
  return path

def load_char_dict(char_vocab_path):
  vocab = [u"<unk>"]
  with codecs.open(char_vocab_path, encoding="utf-8") as f:
    vocab.extend(l.strip() for l in f.readlines())
  char_dict = collections.defaultdict(int)
  char_dict.update({c:i for i, c in enumerate(vocab)})
  return char_dict

def maybe_divide(x, y):
  return 0 if y == 0 else x / float(y)

def projection(inputs, output_size, initializer=None):
  return ffnn(inputs, 0, -1, output_size, dropout=None, output_weights_initializer=initializer)

def highway(inputs, num_layers, dropout):
  for i in range(num_layers):
    with tf.variable_scope("highway_{}".format(i)):
      j, f = tf.split(projection(inputs, 2 * shape(inputs, -1)), 2, -1)
      f = tf.sigmoid(f)
      j = tf.nn.relu(j)
      if dropout is not None:
        j = tf.nn.dropout(j, dropout)
      inputs = f * j + (1 - f) * inputs
  return inputs

def shape(x, dim):
  return x.get_shape()[dim].value or tf.shape(x)[dim]

def ffnn(inputs, num_hidden_layers, hidden_size, output_size, dropout, output_weights_initializer=None):
  if len(inputs.get_shape()) > 3:
    raise ValueError("FFNN with rank {} not supported".format(len(inputs.get_shape())))

  if len(inputs.get_shape()) == 3:
    batch_size = shape(inputs, 0)
    seqlen = shape(inputs, 1)
    emb_size = shape(inputs, 2)
    current_inputs = tf.reshape(inputs, [batch_size * seqlen, emb_size])
  else:
    current_inputs = inputs

  for i in range(num_hidden_layers):
    hidden_weights = tf.get_variable("hidden_weights_{}".format(i), [shape(current_inputs, 1), hidden_size])
    hidden_bias = tf.get_variable("hidden_bias_{}".format(i), [hidden_size])
    current_outputs = tf.nn.relu(tf.nn.xw_plus_b(current_inputs, hidden_weights, hidden_bias))

    if dropout is not None:
      current_outputs = tf.nn.dropout(current_outputs, dropout)
    current_inputs = current_outputs

  output_weights = tf.get_variable("output_weights", [shape(current_inputs, 1), output_size], initializer=output_weights_initializer)
  output_bias = tf.get_variable("output_bias", [output_size])
  outputs = tf.nn.xw_plus_b(current_inputs, output_weights, output_bias)

  if len(inputs.get_shape()) == 3:
    outputs = tf.reshape(outputs, [batch_size, seqlen, output_size])
  return outputs

def cnn(inputs, filter_sizes, num_filters):
  num_words = shape(inputs, 0)
  num_chars = shape(inputs, 1)
  input_size = shape(inputs, 2)
  outputs = []
  for i, filter_size in enumerate(filter_sizes):
    with tf.variable_scope("conv_{}".format(i)):
      w = tf.get_variable("w", [filter_size, input_size, num_filters])
      b = tf.get_variable("b", [num_filters])
    conv = tf.nn.conv1d(inputs, w, stride=1, padding="VALID") # [num_words, num_chars - filter_size, num_filters]
    h = tf.nn.relu(tf.nn.bias_add(conv, b)) # [num_words, num_chars - filter_size, num_filters]
    pooled = tf.reduce_max(h, 1) # [num_words, num_filters]
    outputs.append(pooled)
  return tf.concat(outputs, 1) # [num_words, num_filters * len(filter_sizes)]

def batch_gather(emb, indices):
  batch_size = shape(emb, 0)
  seqlen = shape(emb, 1)
  if len(emb.get_shape()) > 2:
    emb_size = shape(emb, 2)
  else:
    emb_size = 1
  flattened_emb = tf.reshape(emb, [batch_size * seqlen, emb_size])  # [batch_size * seqlen, emb]
  offset = tf.expand_dims(tf.range(batch_size) * seqlen, 1)  # [batch_size, 1]
  gathered = tf.gather(flattened_emb, indices + offset) # [batch_size, num_indices, emb]
  if len(emb.get_shape()) == 2:
    gathered = tf.squeeze(gathered, 2) # [batch_size, num_indices]
  return gathered

class RetrievalEvaluator(object):
  def __init__(self):
    self._num_correct = 0
    self._num_gold = 0
    self._num_predicted = 0

  def update(self, gold_set, predicted_set):
    self._num_correct += len(gold_set & predicted_set)
    self._num_gold += len(gold_set)
    self._num_predicted += len(predicted_set)

  def recall(self):
    return maybe_divide(self._num_correct, self._num_gold)

  def precision(self):
    return maybe_divide(self._num_correct, self._num_predicted)

  def metrics(self):
    recall = self.recall()
    precision = self.precision()
    f1 = maybe_divide(2 * recall * precision, precision + recall)
    return recall, precision, f1

class EmbeddingDictionary(object):
  def __init__(self, info, normalize=True, maybe_cache=None):
    self._size = info["size"]
    self._normalize = normalize
    self._path = info["path"]
    if maybe_cache is not None and maybe_cache._path == self._path:
      assert self._size == maybe_cache._size
      self._embeddings = maybe_cache._embeddings
    else:
      self._embeddings = self.load_embedding_dict(self._path)

  @property
  def size(self):
    return self._size

  def load_embedding_dict(self, path):
    print("Loading word embeddings from {}...".format(path))
    default_embedding = np.zeros(self.size)
    embedding_dict = collections.defaultdict(lambda:default_embedding)
    if len(path) > 0:
      vocab_size = None
      with open(path) as f:
        for i, line in enumerate(f.readlines()):
          word_end = line.find(" ")
          word = line[:word_end]
          embedding = np.fromstring(line[word_end + 1:], np.float32, sep=" ")
          assert len(embedding) == self.size
          embedding_dict[word] = embedding
      if vocab_size is not None:
        assert vocab_size == len(embedding_dict)
      print("Done loading word embeddings.")
    return embedding_dict

  def __getitem__(self, key):
    embedding = self._embeddings[key]
    if self._normalize:
      embedding = self.normalize(embedding)
    return embedding

  def normalize(self, v):
    norm = np.linalg.norm(v)
    if norm > 0:
      return v / norm
    else:
      return v

class CustomLSTMCell(tf.contrib.rnn.RNNCell):
  def __init__(self, num_units, batch_size, dropout):
    self._num_units = num_units
    self._dropout = dropout
    self._dropout_mask = tf.nn.dropout(tf.ones([batch_size, self.output_size]), dropout)
    self._initializer = self._block_orthonormal_initializer([self.output_size] * 3)
    initial_cell_state = tf.get_variable("lstm_initial_cell_state", [1, self.output_size])
    initial_hidden_state = tf.get_variable("lstm_initial_hidden_state", [1, self.output_size])
    self._initial_state = tf.contrib.rnn.LSTMStateTuple(initial_cell_state, initial_hidden_state)

  @property
  def state_size(self):
    return tf.contrib.rnn.LSTMStateTuple(self.output_size, self.output_size)

  @property
  def output_size(self):
    return self._num_units

  @property
  def initial_state(self):
    return self._initial_state

  def __call__(self, inputs, state, scope=None):
    """Long short-term memory cell (LSTM)."""
    with tf.variable_scope(scope or type(self).__name__):  # "CustomLSTMCell"
      c, h = state
      h *= self._dropout_mask
      concat = projection(tf.concat([inputs, h], 1), 3 * self.output_size, initializer=self._initializer)
      i, j, o = tf.split(concat, num_or_size_splits=3, axis=1)
      i = tf.sigmoid(i)
      new_c = (1 - i) * c  + i * tf.tanh(j)
      new_h = tf.tanh(new_c) * tf.sigmoid(o)
      new_state = tf.contrib.rnn.LSTMStateTuple(new_c, new_h)
      return new_h, new_state

  def _orthonormal_initializer(self, scale=1.0):
    def _initializer(shape, dtype=tf.float32, partition_info=None):
      M1 = np.random.randn(shape[0], shape[0]).astype(np.float32)
      M2 = np.random.randn(shape[1], shape[1]).astype(np.float32)
      Q1, R1 = np.linalg.qr(M1)
      Q2, R2 = np.linalg.qr(M2)
      Q1 = Q1 * np.sign(np.diag(R1))
      Q2 = Q2 * np.sign(np.diag(R2))
      n_min = min(shape[0], shape[1])
      params = np.dot(Q1[:, :n_min], Q2[:n_min, :]) * scale
      return params
    return _initializer

  def _block_orthonormal_initializer(self, output_sizes):
    def _initializer(shape, dtype=np.float32, partition_info=None):
      assert len(shape) == 2
      assert sum(output_sizes) == shape[1]
      initializer = self._orthonormal_initializer()
      params = np.concatenate([initializer([shape[0], o], dtype, partition_info) for o in output_sizes], 1)
      return params
    return _initializer


================================================
FILE: worker.py
================================================
#!/usr/bin/env python
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os
import sys
import time

import tensorflow as tf
import coref_model as cm
import util

if __name__ == "__main__":
  config = util.initialize_from_env()
  task_index = int(os.environ["TASK"])

  report_frequency = config["report_frequency"]
  cluster_config = config["cluster"]

  util.set_gpus(cluster_config["gpus"][task_index])

  cluster = tf.train.ClusterSpec(cluster_config["addresses"])
  server = tf.train.Server(cluster,
                           job_name="worker",
                           task_index=task_index)

  # Assigns ops to the local worker by default.
  with tf.device(tf.train.replica_device_setter(worker_device="/job:worker/task:%d" % task_index, cluster=cluster)):
    model = cm.CorefModel(config)
    saver = tf.train.Saver()
    init_op = tf.global_variables_initializer()

  log_dir = config["log_dir"]
  writer = tf.summary.FileWriter(os.path.join(log_dir, "w{}".format(task_index)), flush_secs=20)

  is_chief = (task_index == 0)

  # Create a "supervisor", which oversees the training process.
  sv = tf.train.Supervisor(is_chief=is_chief,
                           logdir=log_dir,
                           init_op=init_op,
                           saver=saver,
                           global_step=model.global_step,
                           save_model_secs=120)

  # The supervisor takes care of session initialization, restoring from
  # a checkpoint, and closing when done or an error occurs.
  with sv.managed_session(server.target) as session:
    model.start_enqueue_thread(session)
    accumulated_loss = 0.0
    initial_time = time.time()
    while not sv.should_stop():
      tf_loss, tf_global_step, _ = session.run([model.loss, model.global_step, model.train_op])
      accumulated_loss += tf_loss

      if tf_global_step % report_frequency == 0:
        total_time = time.time() - initial_time
        steps_per_second = tf_global_step / total_time

        average_loss = accumulated_loss / report_frequency
        print("[{}] loss={:.2f}, steps/s={:.2f}".format(tf_global_step, tf_loss, steps_per_second))
        accumulated_loss = 0.0
        writer.add_summary(util.make_summary({
          "Train Loss": average_loss,
          "Steps per second": steps_per_second
        }))

  # Ask for all the services to stop.
  sv.stop()