-
Notifications
You must be signed in to change notification settings - Fork 1
/
coref_model.py
641 lines (533 loc) · 35.5 KB
/
coref_model.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
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,codecs
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"]) }
self.lm_file = h5py.File(self.config["lm_path"], "r")
self.use_e2e_annealing = self.config['use_e2e_annealing']
self.use_joint_coref = self.config["use_joint_coref"]
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.
input_props.append((tf.int32, [None])) # Pred starts
input_props.append((tf.int32, [None])) # Pred ends
input_props.append((tf.bool,[])) #use_pred_mention
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():
max_step = self.config["max_step"]
curr_step = 0
while True:
pred_ratio = 0.0 if not self.use_e2e_annealing else curr_step * 1.0 / max_step
random.shuffle(train_examples)
for example in train_examples:
use_pred_mentions = True if not self.use_joint_coref else random.random() <= pred_ratio
tensorized_example = self.tensorize_example(example, is_training=True,use_pred_mentions=use_pred_mentions)
feed_dict = dict(zip(self.queue_input_tensors, tensorized_example))
session.run(self.enqueue_op, feed_dict=feed_dict)
curr_step+=1
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("/", ":")
if not file_key in self.lm_file and file_key[:-2] in self.lm_file:
file_key = file_key[:-2]
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_example(self, example, is_training,use_pred_mentions):
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)
if "pred_mentions" in example:
pred_starts, pred_ends = zip(*example["pred_mentions"])
pred_starts, pred_ends= np.array(pred_starts),np.array(pred_ends)
else:
pred_starts,pred_ends = np.array([]),np.array([])
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,pred_starts,pred_ends,use_pred_mentions)
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,pred_starts,pred_ends,use_pred_mentions):
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]
pred_spans = np.logical_and(pred_ends >= word_offset, pred_starts < word_offset + num_words)
pred_starts = pred_starts[pred_spans] - word_offset
pred_ends = pred_ends[pred_spans] - word_offset
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, pred_starts,pred_ends,use_pred_mentions
def get_pred_mention_scores(self, candidate_starts,candidate_ends, pred_mention_starts, pred_mention_ends, pred_mention_scores):
same_start = tf.equal(tf.expand_dims(pred_mention_starts, 1),
tf.expand_dims(candidate_starts, 0)) # [num_labeled, num_candidates]
same_end = tf.equal(tf.expand_dims(pred_mention_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_scores = tf.matmul(tf.expand_dims(pred_mention_scores, 0), tf.to_float(same_span)) # [1, num_candidates]
candidate_scores = tf.squeeze(candidate_scores, 0) # [num_candidates]
return candidate_scores
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,pred_starts, pred_ends,use_pred_mentions):
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)
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_starts = tf.cond(use_pred_mentions,lambda : pred_starts,lambda :candidate_starts)
candidate_ends = tf.cond(use_pred_mentions,lambda :pred_ends,lambda :candidate_ends)
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]
if self.use_joint_coref:
k = tf.to_int32(tf.floor(tf.to_float(tf.shape(context_outputs)[0]) * self.config["top_span_ratio"]))
k = tf.cond(use_pred_mentions,lambda :tf.minimum(util.shape(candidate_starts,0),k),lambda :k)
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_speaker_ids = tf.gather(speaker_ids, top_span_starts) # [k]
else:
k = util.shape(candidate_starts,0)
top_span_starts = candidate_starts
top_span_ends = candidate_ends
top_span_emb = candidate_span_emb
top_span_cluster_ids = candidate_cluster_ids
top_span_mention_scores = candidate_mention_scores
top_span_speaker_ids = tf.gather(speaker_ids, top_span_starts)
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) # []
#loss = tf.Print(loss,[loss])
return [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)
#in case the span from MD is longer than max_span_width, we compute the head attentions for the last max_span_width tokens
span_starts = tf.maximum(span_starts, 1 + span_ends - self.config["max_span_width"]) #[k]
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,scope_name = 'mention_scores'):
with tf.variable_scope(scope_name):
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)
use_pred_mentions = True if not self.use_joint_coref or self.use_e2e_annealing else False
return self.tensorize_example(example, False,use_pred_mentions), example
with open(self.config["eval_path"]) as f:
self.eval_data = [load_line(l) for l in f.readlines()]
print("Loaded {} eval examples.".format(len(self.eval_data)))
def evaluate(self, session, official_stdout=False):
self.load_eval_data()
tp,fn,fp = 0,0,0
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)}
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)
gold_mentions = set([(s, e) for cl in example["clusters"] for s, e in cl])
pred_mentions = set([(s, e) for s, e in zip(top_span_starts, top_span_ends)])
tp += len(gold_mentions & pred_mentions)
fn += len(gold_mentions - pred_mentions)
fp += len(pred_mentions - gold_mentions)
if example_num % 10 == 0:
print("Evaluated {}/{} examples.".format(example_num + 1, len(self.eval_data)))
m_r = float(tp) / (tp + fn)
m_p = float(tp) / (tp + fp)
m_f1 = 2.0 * m_r * m_p / (m_r + m_p)
print("Mention F1: {:.2f}%".format(m_f1 * 100))
print("Mention recall: {:.2f}%".format(m_r * 100))
print("Mention precision: {:.2f}%".format(m_p * 100))
summary_dict = {}
if official_stdout:
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))
average_f1 = average_f1 if official_stdout else f*100
return util.make_summary(summary_dict), average_f1