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 from externals.modified_e2e_coref import util from externals.modified_e2e_coref import coref_ops from externals.modified_e2e_coref import conll from externals.modified_e2e_coref 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