def get_predictions_and_loss(self, word_emb, char_index, text_len, speaker_ids, genre, is_training, gold_starts, gold_ends, cluster_ids):
self.dropout = 1 - (tf.to_float(is_training) * self.config["dropout_rate"])
self.lexical_dropout = 1 - (tf.to_float(is_training) * self.config["lexical_dropout_rate"])
num_sentences = tf.shape(word_emb)[0]
max_sentence_length = tf.shape(word_emb)[1]
text_emb_list = [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]
text_emb_list.append(aggregated_char_emb)
text_emb = tf.concat(text_emb_list, 2)
text_emb = tf.nn.dropout(text_emb, self.lexical_dropout)
text_len_mask = tf.sequence_mask(text_len, maxlen=max_sentence_length)
text_len_mask = tf.reshape(text_len_mask, [num_sentences * max_sentence_length])
self.tm_shape = tf.shape(text_len_mask)
text_outputs = self.encode_sentences(text_emb, text_len, text_len_mask)
text_outputs = tf.nn.dropout(text_outputs, self.dropout)
self.txt_shape = tf.shape(text_outputs)
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_text_emb = self.flatten_emb_by_sentence(text_emb, text_len_mask) # [num_words]
self.flattened_sentence_indices = flattened_sentence_indices
self.emb_shape = tf.shape(flattened_text_emb)
candidate_starts, candidate_ends = coref_ops.spans(
sentence_indices=flattened_sentence_indices,
max_width=self.max_mention_width)
candidate_starts.set_shape([None])
candidate_ends.set_shape([None])
candidate_mention_emb = self.get_mention_emb(flattened_text_emb, text_outputs, candidate_starts, candidate_ends) # [num_candidates, emb]
candidate_mention_scores = self.get_mention_scores(candidate_mention_emb) # [num_mentions, 1]
candidate_mention_scores = tf.squeeze(candidate_mention_scores, 1) # [num_mentions]
k = tf.to_int32(tf.floor(tf.to_float(tf.shape(text_outputs)[0]) * self.config["mention_ratio"]))
# predicted_mention_indices, context_starts, context_ends, context_length = coref_ops.extract_mentions(candidate_mention_scores,
# candidate_starts,
# candidate_ends,
# k,
# self.max_context_width) # ([k], [k])
predicted_mention_indices = coref_ops.extract_mentions(candidate_mention_scores,
candidate_starts,
candidate_ends,
k) # ([k], [k])
predicted_mention_indices.set_shape([None])
# context_starts.set_shape([None])
# context_ends.set_shape([None])
# context_length.set_shape([None])
mention_starts = tf.gather(candidate_starts, predicted_mention_indices) # [num_mentions]
mention_ends = tf.gather(candidate_ends, predicted_mention_indices) # [num_mentions]
mention_emb = tf.gather(candidate_mention_emb, predicted_mention_indices) # [num_mentions, emb]
mention_scores = tf.gather(candidate_mention_scores, predicted_mention_indices) # [num_mentions]
mention_start_emb = tf.gather(text_outputs, mention_starts) # [num_mentions, emb]
mention_end_emb = tf.gather(text_outputs, mention_ends) # [num_mentions, emb]
mention_speaker_ids = tf.gather(speaker_ids, mention_starts) # [num_mentions]
context_starts = tf.maximum(mention_starts - 5, 0)
context_ends = tf.minimum(mention_ends + 5, util.shape(text_outputs, 0) - 1)
context_start_emb = tf.gather(text_outputs, context_starts) # [num_mentions, emb]
context_end_emb = tf.gather(text_outputs, context_ends) # [num_mentions, emb]
max_antecedents = self.config["max_antecedents"]
antecedents, antecedent_labels, antecedents_len = coref_ops.antecedents(mention_starts,
mention_ends,
gold_starts,
gold_ends,
cluster_ids,
max_antecedents)# ([num_mentions, max_ant], [num_mentions, max_ant + 1], [num_mentions]
antecedents.set_shape([None, None])
antecedent_labels.set_shape([None, None])
antecedents_len.set_shape([None])
antecedent_scores = self.get_context_antecedent_scores(mention_emb,
mention_scores,
antecedents,
antecedents_len,
mention_starts,
mention_ends,
mention_speaker_ids,
genre_emb,
context_starts,
context_ends,
text_outputs,
flattened_text_emb) # [num_mentions, max_ant + 1]
loss = self.softmax_loss(antecedent_scores, antecedent_labels) # [num_mentions]
loss = tf.reduce_sum(loss) # []
return [
candidate_starts,
candidate_ends,
candidate_mention_scores,
mention_starts,
mention_ends,
antecedents,
antecedent_scores
], loss
def get_predictions_and_loss(self,
word_emb,
char_index,
text_len,
speaker_ids,
genre,
is_training,
gold_starts,
gold_ends,
cluster_ids,
span_labels,
span_seq):
self.gold_starts = gold_starts
self.cluster_ids = cluster_ids
span_seq_bin = tf.where(tf.greater(span_seq, 0), tf.ones_like(span_seq), tf.zeros_like(span_seq))
span_labels_bin = tf.one_hot(span_seq_bin, 2) # [num_mention, 2]
self.dropout = 1 - (tf.to_float(is_training) * self.config["dropout_rate"])
self.lexical_dropout = 1 - (tf.to_float(is_training) * self.config["lexical_dropout_rate"])
num_sentences = tf.shape(word_emb)[0]
max_sentence_length = tf.shape(word_emb)[1]
text_emb_list = [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]
text_emb_list.append(aggregated_char_emb)
text_emb = tf.concat(text_emb_list, 2)
text_emb = tf.nn.dropout(text_emb, self.lexical_dropout)
text_len_mask = tf.sequence_mask(text_len, maxlen=max_sentence_length)
text_len_mask = tf.reshape(text_len_mask, [num_sentences * max_sentence_length])
# self.text_len_mask = text_len_mask[0]
text_outputs = self.encode_sentences(text_emb, text_len, text_len_mask)
text_outputs = tf.nn.dropout(text_outputs, self.dropout)
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_text_emb = self.flatten_emb_by_sentence(text_emb, text_len_mask) # [num_words]
self.flattened_sentence_indices = flattened_sentence_indices
candidate_starts, candidate_ends = coref_ops.regression(
sentence_indices=flattened_sentence_indices,
max_width=self.max_mention_width)
candidate_starts.set_shape([None])
candidate_ends.set_shape([None])
candidate_mention_emb = self.get_mention_emb(flattened_text_emb, text_outputs, candidate_starts, candidate_ends) # [num_candidates, emb]
candidate_mention_logits = self.get_mention_scores(candidate_mention_emb) # [num_mentions, 2]
# mention_loss = tf.losses.softmax_cross_entropy(span_labels_bin, candidate_mention_logits)
# mention_loss = tf.nn.softmax_cross_entropy_with_logits(logits=candidate_mention_logits,
# labels=span_labels_bin)
# mention_loss = tf.reduce_sum(mention_loss)
# mention_loss = tf.reduce_sum(mention_loss * tf.cast(span_seq_bin, tf.float32))
# candidate_mention_scores = tf.squeeze(tf.gather(tf.transpose(candidate_mention_logits), 1)) -\
# tf.squeeze(tf.gather(tf.transpose(candidate_mention_logits), 0))
candidate_mention_scores = tf.squeeze(tf.gather(tf.transpose(candidate_mention_logits), 1)) # [num_mentions]
k = tf.to_int32(tf.floor(tf.to_float(tf.shape(text_outputs)[0]) * self.config["mention_ratio"]))
# k = tf.cond(is_training, lambda: k, lambda: tf.shape(text_outputs)[0])
# k = tf.cond(is_training, lambda: tf.minimum(k, 250), lambda: k)
k = tf.minimum(k, 250)
predicted_mention_indices = coref_ops.extract_mentions(candidate_mention_scores, candidate_starts, candidate_ends, k) # ([k], [k])
predicted_mention_indices.set_shape([None])
mention_starts = tf.gather(candidate_starts, predicted_mention_indices) # [num_mentions]
mention_ends = tf.gather(candidate_ends, predicted_mention_indices) # [num_mentions]
mention_emb = tf.gather(candidate_mention_emb, predicted_mention_indices) # [num_mentions, emb]
mention_scores = tf.gather(candidate_mention_scores, predicted_mention_indices) # [num_mentions, 1]
mention_start_emb = tf.gather(text_outputs, mention_starts) # [num_mentions, emb]
mention_end_emb = tf.gather(text_outputs, mention_ends) # [num_mentions, emb]
mention_speaker_ids = tf.gather(speaker_ids, mention_starts) # [num_mentions]
max_antecedents = self.config["max_antecedents"]
antecedents, antecedent_labels, antecedents_len = coref_ops.antecedents(mention_starts,
mention_ends,
gold_starts,
gold_ends,
cluster_ids,
max_antecedents * 10) # ([num_mentions, max_ant], [num_mentions, max_ant + 1], [num_mentions]
antecedents.set_shape([None, None])
antecedent_labels.set_shape([None, None])
antecedents_len.set_shape([None])
antecedent_features = self.get_antecedent_features(mention_emb,
mention_scores,
antecedents,
antecedents_len,
mention_starts,
mention_ends,
mention_speaker_ids,
genre_emb) # [num_mentions, max_ant + 1]
# antecedent_loss = tf.reduce_sum(self.softmax_loss(antecedent_scores, antecedent_labels))
# antecedent_scores = tf.expand_dims(antecedent_scores, 0)
# mention_loss = tf.losses.softmax_cross_entropy(span_labels_bin, mention_logits)
# mention_scores = tf.squeeze(tf.gather(tf.transpose(mention_logits), 1))
span_emb = tf.expand_dims(mention_emb, 0)
# span_labels = tf.gather(span_labels, predicted_mention_indices)
span_seq_sorted = coref_ops.tagging(tf.gather(span_seq, predicted_mention_indices))
predicted_mention_indices.set_shape([None])
span_labels = tf.expand_dims(tf.one_hot(span_seq_sorted, 100), 0)
self.span_labels = tf.reduce_sum(span_labels)
self.span_seq = span_seq_sorted
# text_conv = tf.expand_dims(text_outputs, 0)
# text_conv = tf.expand_dims(tf.concat([text_outputs, flattened_text_emb], 1), 0)
# text_conv = util.cnn_name(text_conv, [5], 100, 'tag_conv')
# text_conv = tf.nn.dropout(text_conv, self.dropout)
# self.mention_scores = mention_scores
outputs = self.gnc_tagging(span_emb, span_labels, span_seq_sorted, mention_scores, antecedent_features, k) # [1, num_words, 100] ?
# x = tf.matmul(outputs, tf.zeros([10, 80]))
logits, antecedent_scores = tf.split(outputs, [100, k + 1], 2)
self.logits_shape = tf.shape(logits)
self.antecedent_scores_shape = tf.shape(antecedent_scores)
# antecedent_scores = tf.gather(attention[0], tf.range(k))
predictions = tf.argmax(logits, axis=2) # [1, num_words] ?
# loss = tf.losses.softmax_cross_entropy(span_labels, tf.squeeze(logits))
# loss = tf.reduce_sum(tf.nn.softmax_cross_entropy_with_logits(logits=tf.squeeze(logits), labels=span_labels))
# span_seq_expanded = tf.expand_dims(span_seq_sorted, 0)
# loss = tf.losses.log_loss(span_labels, tf.squeeze(tf.nn.softmax(logits, dim=2))) + .1 * mention_loss
# self.tag_seq = tag_seq
# tagging_loss = tf.reduce_sum(self.sigmoid_margin(logits, tf.to_float(span_labels), tf.to_float(k)))
tagging_loss = tf.reduce_sum(self.exp_loss_margin(logits, tf.to_float(span_labels), 2))
antecedent_loss = tf.reduce_sum(self.softmax_loss(antecedent_scores[0], antecedent_labels))
# antecedent_loss = tf.reduce_sum(self.exp_loss_margin(antecedent_scores[0], antecedent_labels, 1))
# y = tf.squeeze(tf.nn.softmax(logits, dim=2))
# loss = -tf.reduce_sum(span_labels * tf.log(y) + (1 - span_labels) * tf.log(1 - y))
# loss = -tf.reduce_sum((1 - span_labels) * tf.log(1 - y))
self.tagging_loss = tagging_loss
# self.mention_loss = mention_loss
self.antecedent_loss = antecedent_loss
loss = tagging_loss + antecedent_loss # + mention_loss
self.p = predictions
return [
candidate_starts,
candidate_ends,
candidate_mention_scores,
mention_starts,
mention_ends,
predictions,
antecedents,
antecedent_scores[0]
], loss
def get_predictions_and_loss(self, word_emb, char_index, text_len, speaker_ids, genre, is_training, gold_starts, gold_ends, cluster_ids):
self.dropout = 1 - (tf.to_float(is_training) * self.config["dropout_rate"])
self.lexical_dropout = 1 - (tf.to_float(is_training) * self.config["lexical_dropout_rate"])
num_sentences = tf.shape(word_emb)[0]
max_sentence_length = tf.shape(word_emb)[1]
text_emb_list = [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]
text_emb_list.append(aggregated_char_emb)
text_emb = tf.concat(text_emb_list, 2)
text_emb = tf.nn.dropout(text_emb, self.lexical_dropout)
text_len_mask = tf.sequence_mask(text_len, maxlen=max_sentence_length)
text_len_mask = tf.reshape(text_len_mask, [num_sentences * max_sentence_length])
text_outputs = self.encode_sentences(text_emb, text_len, text_len_mask)
text_outputs = tf.nn.dropout(text_outputs, self.dropout)
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_text_emb = self.flatten_emb_by_sentence(text_emb, text_len_mask) # [num_words]
candidate_starts, candidate_ends = coref_ops.spans(
sentence_indices=flattened_sentence_indices,
max_width=self.max_mention_width)
candidate_starts.set_shape([None])
candidate_ends.set_shape([None])
candidate_mention_emb = self.get_mention_emb(flattened_text_emb, text_outputs, candidate_starts, candidate_ends) # [num_candidates, emb]
candidate_mention_scores = self.get_mention_scores(candidate_mention_emb) # [num_mentions, 1]
candidate_mention_scores = tf.squeeze(candidate_mention_scores, 1) # [num_mentions]
k = tf.to_int32(tf.floor(tf.to_float(tf.shape(text_outputs)[0]) * self.config["mention_ratio"]))
predicted_mention_indices = coref_ops.extract_mentions(candidate_mention_scores, candidate_starts, candidate_ends, k) # ([k], [k])
predicted_mention_indices.set_shape([None])
mention_starts = tf.gather(candidate_starts, predicted_mention_indices) # [num_mentions]
mention_ends = tf.gather(candidate_ends, predicted_mention_indices) # [num_mentions]
mention_emb = tf.gather(candidate_mention_emb, predicted_mention_indices) # [num_mentions, emb]
mention_scores = tf.gather(candidate_mention_scores, predicted_mention_indices) # [num_mentions]
mention_start_emb = tf.gather(text_outputs, mention_starts) # [num_mentions, emb]
mention_end_emb = tf.gather(text_outputs, mention_ends) # [num_mentions, emb]
mention_speaker_ids = tf.gather(speaker_ids, mention_starts) # [num_mentions]
max_antecedents = self.config["max_antecedents"]
antecedents, antecedent_labels, antecedents_len = coref_ops.antecedents(mention_starts, mention_ends, gold_starts, gold_ends, cluster_ids, max_antecedents) # ([num_mentions, max_ant], [num_mentions, max_ant + 1], [num_mentions]
antecedents.set_shape([None, None])
antecedent_labels.set_shape([None, None])
antecedents_len.set_shape([None])
antecedent_scores = self.get_antecedent_scores(mention_emb, mention_scores, antecedents, antecedents_len, mention_starts, mention_ends, mention_speaker_ids, genre_emb) # [num_mentions, max_ant + 1]
loss = self.softmax_loss(antecedent_scores, antecedent_labels) # [num_mentions]
loss = tf.reduce_sum(loss) # []
return [candidate_starts, candidate_ends, candidate_mention_scores, mention_starts, mention_ends, antecedents, antecedent_scores], loss
def get_predictions_and_loss(self, word_emb, char_index, text_len,
speaker_ids, genre, is_training, gold_starts,
gold_ends, cluster_ids):
self.gold_starts = gold_starts
self.gold_ends = gold_ends
self.cluster_ids = cluster_ids
self.dropout = 1 - (tf.to_float(is_training) *
self.config["dropout_rate"])
self.lexical_dropout = 1 - (tf.to_float(is_training) *
self.config["lexical_dropout_rate"])
num_sentences = tf.shape(word_emb)[0]
max_sentence_length = tf.shape(word_emb)[1]
text_emb_list = [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]
text_emb_list.append(aggregated_char_emb)
text_emb = tf.concat(text_emb_list, 2)
text_emb = tf.nn.dropout(text_emb, self.lexical_dropout)
text_len_mask = tf.sequence_mask(text_len, maxlen=max_sentence_length)
text_len_mask = tf.reshape(text_len_mask,
[num_sentences * max_sentence_length])
# self.text_len_mask = text_len_mask[0]
text_outputs = self.encode_sentences(text_emb, text_len, text_len_mask)
text_outputs = tf.nn.dropout(text_outputs, self.dropout)
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_text_emb = self.flatten_emb_by_sentence(
text_emb, text_len_mask) # [num_words]
self.flattened_sentence_indices = flattened_sentence_indices
word_nemb = tf.concat([text_outputs, flattened_text_emb], 1)
# word_nemb = tf.reshape(word_nemb, [tf.shape(text_outputs)[0], tf.shape(text_outputs)[1] + tf.shape(flattened_text_emb)[1]])
with tf.variable_scope("conv_score"):
# nemb_size = util.shape(word_nemb, 1)
# w = tf.get_variable("w", [3, nemb_size, 150])
# b = tf.get_variable("b", [150])
# conv = tf.nn.conv1d(tf.expand_dims(word_nemb, 0), w, stride=1, padding="SAME")
# h = tf.nn.relu(tf.nn.bias_add(conv, b))
candidate_word_scores = util.projection(word_nemb, 2)
start_scores, end_scores = tf.split(candidate_word_scores, [1, 1],
1)
start_scores = tf.reshape(start_scores, [-1])
end_scores = tf.reshape(end_scores, [-1])
candidate_starts, candidate_ends = coref_ops.spans(
sentence_indices=flattened_sentence_indices,
max_width=self.max_mention_width)
candidate_starts.set_shape([None])
candidate_ends.set_shape([None])
candidate_mention_emb = self.get_mention_emb(
flattened_text_emb, text_outputs, candidate_starts,
candidate_ends) # [num_candidates, emb]
candidate_mention_scores = self.get_mention_scores(
candidate_mention_emb) # [num_mentions, 1]
candidate_mention_scores = tf.squeeze(candidate_mention_scores,
1) # [num_mentions]
candidate_mention_scores += tf.gather(start_scores,
candidate_starts) + tf.gather(
end_scores, candidate_ends)
k = tf.to_int32(
tf.floor(
tf.to_float(tf.shape(text_outputs)[0]) *
self.config["mention_ratio"]))
k = tf.minimum(k, self.config["max_antecedents"])
predicted_mention_indices = coref_ops.extract_mentions(
candidate_mention_scores, candidate_starts, candidate_ends,
k) # ([k], [k])
predicted_mention_indices.set_shape([None])
mention_starts = tf.gather(candidate_starts,
predicted_mention_indices) # [num_mentions]
mention_ends = tf.gather(candidate_ends,
predicted_mention_indices) # [num_mentions]
mention_emb = tf.gather(
candidate_mention_emb,
predicted_mention_indices) # [num_mentions, emb]
# mention_emb = tf.reshape(mention_emb, [k, -1])
mention_scores = tf.gather(candidate_mention_scores,
predicted_mention_indices) # [num_mentions]
word_scores = tf.gather(candidate_word_scores,
predicted_mention_indices)
mention_start_emb = tf.gather(text_outputs,
mention_starts) # [num_mentions, emb]
mention_end_emb = tf.gather(text_outputs,
mention_ends) # [num_mentions, emb]
mention_speaker_ids = tf.gather(speaker_ids,
mention_starts) # [num_mentions]
max_antecedents = self.config["max_antecedents"]
antecedents, antecedent_labels, antecedents_len = coref_ops.antecedents(
mention_starts, mention_ends, gold_starts, gold_ends, cluster_ids,
k
) # ([num_mentions, max_ant], [num_mentions, max_ant + 1], [num_mentions]
antecedents.set_shape([None, None])
antecedent_labels.set_shape([None, None])
antecedents_len.set_shape([None])
antecedent_scores = self.get_antecedent_scores(
mention_emb, word_nemb, mention_scores, antecedents,
antecedents_len, mention_starts, mention_ends, mention_speaker_ids,
speaker_ids, genre_emb, tf.expand_dims(start_scores, 1),
tf.expand_dims(end_scores, 1), k) # [num_mentions, max_ant + 1]
loss = self.softmax_loss(antecedent_scores,
antecedent_labels) # [num_mentions]
# loss = self.exp_loss_margin(antecedent_scores, antecedent_labels) # [num_mentions]
loss = tf.reduce_sum(loss) # []
return [
candidate_starts, candidate_ends, candidate_mention_scores,
mention_starts, mention_ends, antecedents, antecedent_scores
], loss
def get_predictions_and_loss(self, word_emb, char_index, text_len, speaker_ids, genre, is_training, gold_starts, gold_ends, cluster_ids, pos_tags, ner_tags, categories, ner_ids, cat_glove, domain_labels, l):
self.dropout = 1 - (tf.to_float(is_training) * self.config["dropout_rate"])
self.lexical_dropout = 1 - (tf.to_float(is_training) * self.config["lexical_dropout_rate"])
num_sentences = tf.shape(word_emb)[0]
max_sentence_length = tf.shape(word_emb)[1]
text_emb_list = [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]
text_emb_list.append(aggregated_char_emb)
if self.config["use_pos_tag"]:
text_emb_list.append(pos_tags)
if self.config["use_ner_g"]:
text_emb_list.append(ner_tags)
if self.config["use_categories"]:
text_emb_list.append(categories)
if self.config["use_categories_glove"]:
text_emb_list.append(cat_glove)
text_emb = tf.concat(text_emb_list, 2)
text_emb = tf.nn.dropout(text_emb, self.lexical_dropout)
text_len_mask = tf.sequence_mask(text_len, maxlen=max_sentence_length)
text_len_mask = tf.reshape(text_len_mask, [num_sentences * max_sentence_length])
text_outputs = self.encode_sentences(text_emb, text_len, text_len_mask)
text_outputs = tf.nn.dropout(text_outputs, self.dropout)
genre_emb = tf.gather(tf.get_variable("genre_embeddings", [len(self.genres), self.config["feature_size"]]), genre) # [emb]
# print "------------------------------"
# print "GENRE EMB"
# print genre_emb.shape, genre
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_text_emb = self.flatten_emb_by_sentence(text_emb, text_len_mask) # [num_words]
candidate_starts, candidate_ends = coref_ops.spans(
sentence_indices=flattened_sentence_indices,
max_width=self.max_mention_width)
candidate_starts.set_shape([None])
candidate_ends.set_shape([None])
# get_mention_scores call util.ffnn
candidate_mention_emb = self.get_mention_emb(flattened_text_emb, text_outputs, candidate_starts, candidate_ends) # [num_candidates, emb]
candidate_mention_scores = self.get_mention_scores(candidate_mention_emb) # [num_mentions, 1]
candidate_mention_scores = tf.squeeze(candidate_mention_scores, 1) # [num_mentions]
k = tf.to_int32(tf.floor(tf.to_float(tf.shape(text_outputs)[0]) * self.config["mention_ratio"]))
predicted_mention_indices = coref_ops.extract_mentions(candidate_mention_scores, candidate_starts, candidate_ends, k) # ([k], [k])
predicted_mention_indices.set_shape([None])
mention_starts = tf.gather(candidate_starts, predicted_mention_indices) # [num_mentions]
mention_ends = tf.gather(candidate_ends, predicted_mention_indices) # [num_mentions]
mention_emb = tf.gather(candidate_mention_emb, predicted_mention_indices) # [num_mentions, emb]
mention_scores = tf.gather(candidate_mention_scores, predicted_mention_indices) # [num_mentions]
mention_start_emb = tf.gather(text_outputs, mention_starts) # [num_mentions, emb]
mention_end_emb = tf.gather(text_outputs, mention_ends) # [num_mentions, emb]
mention_speaker_ids = tf.gather(speaker_ids, mention_starts) # [num_mentions]
mention_ner_ids = tf.gather(ner_ids, mention_starts)
max_antecedents = self.config["max_antecedents"]
antecedents, antecedent_labels, antecedents_len = coref_ops.antecedents(mention_starts, mention_ends, gold_starts, gold_ends, cluster_ids, max_antecedents) # ([num_mentions, max_ant], [num_mentions, max_ant + 1], [num_mentions]
antecedents.set_shape([None, None])
antecedent_labels.set_shape([None, None])
antecedents_len.set_shape([None])
# get_antecedent_scores calls util.ffnn
antecedent_scores, pair_emb = self.get_antecedent_scores(mention_emb, mention_scores, antecedents, antecedents_len, mention_starts, mention_ends, mention_speaker_ids, genre_emb, mention_ner_ids) # [num_mentions, max_ant + 1]
# antecedent scores are floating points
# antecedent labels are True/False
# softmax_loss converts True/False into floating points
loss = self.softmax_loss(antecedent_scores, antecedent_labels) # [num_mentions]
loss = tf.reduce_sum(loss) # []
print "---------------------------------------"
print "PAIR EMB"
print pair_emb.shape
print "---------------------------------------"
print "CANDIDATE"
print candidate_mention_emb.shape
print "---------------------------------------"
# if self.config["use_dann"]:
d_logits = util.dann(candidate_mention_emb, self.config["ffnn_size"],
len(self.genres), self.dropout, l=l, name="1")
# elif self.config["use_dann_pairwise"]:
d_logits2 = util.dann(pair_emb, self.config["ffnn_size"],
len(self.genres), self.dropout, l=l, name="2")
print "---------------------------------------"
print "D_LOGITS"
print d_logits2.shape
print "---------------------------------------"
d_probs2 = tf.nn.softmax(d_logits2)
d_probs = tf.nn.softmax(d_logits) # P(genre | mentions)
neg_ll = -tf.log(tf.clip_by_value(d_probs,1e-10,1.0)) # N x 7
N = tf.shape(neg_ll)[0]
# argmax of d_probs are predicted domains
pred_domains = tf.argmax(d_probs, 1)
# changing 1 x 7 domain labels to N x 7
tiled_domain_labels = tf.tile(tf.expand_dims(domain_labels, 0), [N, 1])
# converting tiles to be comparable to pred_domains
gold_domains = tf.argmax(tiled_domain_labels, 1)
# multiply neg_ll with tiled_domain_labels
pairwise_loss = tf.multiply(neg_ll, tiled_domain_labels) # N x 7
pairwise_loss_reduced = tf.reduce_sum(pairwise_loss, 0)
domain_loss = tf.reduce_sum(tf.divide(pairwise_loss_reduced, tf.cast(N, tf.float32)))
domain_loss_reduce_mean = tf.reduce_sum(tf.reduce_mean(pairwise_loss, 0))
correct_domain_predictions = tf.equal(pred_domains, gold_domains)
domain_accuracy = tf.reduce_mean(tf.cast(correct_domain_predictions, tf.float32))
values = [domain_accuracy, domain_loss_reduce_mean, pairwise_loss_reduced, N, neg_ll, d_logits2, d_logits]
return [candidate_starts, candidate_ends, candidate_mention_scores, mention_starts, mention_ends, antecedents, antecedent_scores, antecedent_labels, genre], loss, domain_loss, pred_domains, values
def get_predictions_and_loss(self, word_emb, char_index, text_len,
speaker_ids, genre, is_training, gold_starts,
gold_ends, cluster_ids):
self.gold_starts = gold_starts
self.gold_ends = gold_ends
self.cluster_ids = cluster_ids
self.dropout = 1 - (tf.to_float(is_training) *
self.config["dropout_rate"])
self.lexical_dropout = 1 - (tf.to_float(is_training) *
self.config["lexical_dropout_rate"])
num_sentences = tf.shape(word_emb)[0]
max_sentence_length = tf.shape(word_emb)[1]
text_emb_list = [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]
text_emb_list.append(aggregated_char_emb)
text_emb = tf.concat(text_emb_list, 2)
text_emb = tf.nn.dropout(text_emb, self.lexical_dropout)
text_len_mask = tf.sequence_mask(text_len, maxlen=max_sentence_length)
text_len_mask = tf.reshape(text_len_mask,
[num_sentences * max_sentence_length])
# self.text_len_mask = text_len_mask[0]
text_outputs = self.encode_sentences(text_emb, text_len, text_len_mask)
text_outputs = tf.nn.dropout(text_outputs, self.dropout)
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_text_emb = self.flatten_emb_by_sentence(
text_emb, text_len_mask) # [num_words]
self.flattened_sentence_indices = flattened_sentence_indices
candidate_starts, candidate_ends = coref_ops.spans(
sentence_indices=flattened_sentence_indices,
max_width=self.max_mention_width)
candidate_starts.set_shape([None])
candidate_ends.set_shape([None])
candidate_mention_emb = self.get_mention_emb(
flattened_text_emb, text_outputs, candidate_starts,
candidate_ends) # [num_candidates, emb]
candidate_mention_scores = self.get_mention_scores(
candidate_mention_emb) # [num_mentions, 1]
candidate_mention_scores = tf.squeeze(candidate_mention_scores,
1) # [num_mentions]
k = tf.to_int32(
tf.floor(
tf.to_float(tf.shape(text_outputs)[0]) *
self.config["mention_ratio"]))
k = tf.minimum(k, self.config["max_antecedents"])
predicted_mention_indices = coref_ops.extract_mentions(
candidate_mention_scores, candidate_starts, candidate_ends,
k) # ([k], [k])
predicted_mention_indices.set_shape([None])
mention_starts = tf.gather(candidate_starts,
predicted_mention_indices) # [num_mentions]
mention_ends = tf.gather(candidate_ends,
predicted_mention_indices) # [num_mentions]
mention_emb = tf.gather(
candidate_mention_emb,
predicted_mention_indices) # [num_mentions, emb]
mention_scores = tf.gather(candidate_mention_scores,
predicted_mention_indices) # [num_mentions]
mention_start_emb = tf.gather(text_outputs,
mention_starts) # [num_mentions, emb]
mention_end_emb = tf.gather(text_outputs,
mention_ends) # [num_mentions, emb]
mention_speaker_ids = tf.gather(speaker_ids,
mention_starts) # [num_mentions]
max_antecedents = self.config["max_antecedents"]
antecedents, antecedent_labels, antecedents_len = coref_ops.antecedents(
mention_starts, mention_ends, gold_starts, gold_ends, cluster_ids,
max_antecedents
) # ([num_mentions, max_ant], [num_mentions, max_ant + 1], [num_mentions]
antecedents.set_shape([None, None])
antecedent_labels.set_shape([None, None])
antecedents_len.set_shape([None])
A = tf.eye(k)
with tf.variable_scope("layer_1"):
antecedent_scores_1 = self.get_antecedent_scores(
mention_emb, mention_scores, antecedents, antecedents_len,
mention_starts, mention_ends, mention_speaker_ids, genre_emb,
k) # [num_mentions, max_ant + 1]
DAD_1 = self.get_DAD(A, antecedent_scores_1, k)
mention_emb_1 = tf.nn.relu(
util.projection(tf.matmul(DAD_1, mention_emb), 200))
with tf.variable_scope("layer_2"):
antecedent_scores_2 = self.get_antecedent_scores(
mention_emb_1, mention_scores, antecedents, antecedents_len,
mention_starts, mention_ends, mention_speaker_ids, genre_emb,
k) # [num_mentions, max_ant + 1]
DAD_2 = self.get_DAD(A, antecedent_scores_2, k)
mention_emb_2 = tf.nn.relu(
util.projection(tf.matmul(DAD_2, mention_emb), 200))
with tf.variable_scope("layer_3"):
antecedent_scores = self.get_antecedent_scores(
mention_emb_2, mention_scores, antecedents, antecedents_len,
mention_starts, mention_ends, mention_speaker_ids, genre_emb,
k) # [num_mentions, max_ant + 1]
antecedent_scores += antecedent_scores_1
loss = self.softmax_loss(antecedent_scores,
antecedent_labels) # [num_mentions]
# loss = self.exp_loss_margin(antecedent_scores, antecedent_labels) # [num_mentions]
loss = tf.reduce_sum(loss) # []
return [
candidate_starts, candidate_ends, candidate_mention_scores,
mention_starts, mention_ends, antecedents, antecedent_scores
], loss
