def get_antecedent_features(self,
mention_emb,
mention_scores,
antecedents,
antecedents_len,
mention_starts,
mention_ends,
mention_speaker_ids,
genre_emb):
num_mentions = util.shape(mention_emb, 0)
max_antecedents = util.shape(antecedents, 1)
feature_emb_list = []
if self.config["use_metadata"]:
antecedent_speaker_ids = tf.gather(mention_speaker_ids, antecedents) # [num_mentions, max_ant]
same_speaker = tf.equal(tf.expand_dims(mention_speaker_ids, 1), antecedent_speaker_ids) # [num_mentions, max_ant]
speaker_pair_emb = tf.gather(tf.get_variable("same_speaker_emb", [2, self.config["feature_size"]]), tf.to_int32(same_speaker)) # [num_mentions, max_ant, emb]
feature_emb_list.append(speaker_pair_emb)
tiled_genre_emb = tf.tile(tf.expand_dims(tf.expand_dims(genre_emb, 0), 0), [num_mentions, max_antecedents, 1]) # [num_mentions, max_ant, emb]
feature_emb_list.append(tiled_genre_emb)
if self.config["use_features"]:
target_indices = tf.range(num_mentions) # [num_mentions]
mention_distance = tf.expand_dims(target_indices, 1) - antecedents # [num_mentions, max_ant]
mention_distance_bins = coref_ops.distance_bins(mention_distance) # [num_mentions, max_ant]
mention_distance_bins.set_shape([None, None])
mention_distance_emb = tf.gather(tf.get_variable("mention_distance_emb", [10, self.config["feature_size"]]), mention_distance_bins) # [num_mentions, max_ant]
feature_emb_list.append(mention_distance_emb)
feature_emb = tf.concat(feature_emb_list, 2) # [num_mentions, max_ant, emb]
feature_emb = tf.nn.dropout(feature_emb, self.dropout) # [num_mentions, max_ant, emb]
return feature_emb # [num_mentions, max_ant + 1]
def get_antecedent_scores(self, mention_emb, mention_scores, antecedents, antecedents_len, mention_starts, mention_ends, mention_speaker_ids, genre_emb, mention_ner_ids):
num_mentions = util.shape(mention_emb, 0)
max_antecedents = util.shape(antecedents, 1)
feature_emb_list = []
if self.config["use_metadata"]:
antecedent_speaker_ids = tf.gather(mention_speaker_ids, antecedents) # [num_mentions, max_ant]
same_speaker = tf.equal(tf.expand_dims(mention_speaker_ids, 1), antecedent_speaker_ids) # [num_mentions, max_ant]
speaker_pair_emb = tf.gather(tf.get_variable("same_speaker_emb", [2, self.config["feature_size"]]), tf.to_int32(same_speaker)) # [num_mentions, max_ant, emb]
feature_emb_list.append(speaker_pair_emb)
# tile is duplicating data [a b c d] --> [a b c d a b c d]
tiled_genre_emb = tf.tile(tf.expand_dims(tf.expand_dims(genre_emb, 0), 0), [num_mentions, max_antecedents, 1]) # [num_mentions, max_ant, emb]
feature_emb_list.append(tiled_genre_emb)
if self.config["use_features"]:
target_indices = tf.range(num_mentions) # [num_mentions]
mention_distance = tf.expand_dims(target_indices, 1) - antecedents # [num_mentions, max_ant]
mention_distance_bins = coref_ops.distance_bins(mention_distance) # [num_mentions, max_ant]
mention_distance_bins.set_shape([None, None])
mention_distance_emb = tf.gather(tf.get_variable("mention_distance_emb", [10, self.config["feature_size"]]), mention_distance_bins) # [num_mentions, max_ant]
feature_emb_list.append(mention_distance_emb)
if self.config["use_ner_phi"]:
antecedent_ner_ids = tf.gather(mention_ner_ids, antecedents)
same_ner = tf.equal(tf.expand_dims(mention_ner_ids, 1), antecedent_ner_ids)
ner_pair_emb = tf.gather(tf.get_variable("same_ner_emb", [2, self.config["feature_size"]]), tf.to_int32(same_ner))
feature_emb_list.append(ner_pair_emb)
# phi(i, j)
feature_emb = tf.concat(feature_emb_list, 2) # [num_mentions, max_ant, emb]
feature_emb = tf.nn.dropout(feature_emb, self.dropout) # [num_mentions, max_ant, emb]
# g_i
antecedent_emb = tf.gather(mention_emb, antecedents) # [num_mentions, max_ant, emb]
# g_j
target_emb_tiled = tf.tile(tf.expand_dims(mention_emb, 1), [1, max_antecedents, 1]) # [num_mentions, max_ant, emb]
# g_i . g_j
similarity_emb = antecedent_emb * target_emb_tiled # [num_mentions, max_ant, emb]
# [g_i, g_j, g_i . g_j, phi(i, j)]
pair_emb = tf.concat([target_emb_tiled, antecedent_emb, similarity_emb, feature_emb], 2) # [num_mentions, max_ant, emb]
with tf.variable_scope("iteration"):
with tf.variable_scope("antecedent_scoring"):
antecedent_scores = util.ffnn(pair_emb, self.config["ffnn_depth"], self.config["ffnn_size"], 1, self.dropout) # [num_mentions, max_ant, 1]
antecedent_scores = tf.squeeze(antecedent_scores, 2) # [num_mentions, max_ant]
antecedent_mask = tf.log(tf.sequence_mask(antecedents_len, max_antecedents, dtype=tf.float32)) # [num_mentions, max_ant]
antecedent_scores += antecedent_mask # [num_mentions, max_ant]
antecedent_scores += tf.expand_dims(mention_scores, 1) + tf.gather(mention_scores, antecedents) # [num_mentions, max_ant]
antecedent_scores = tf.concat([tf.zeros([util.shape(mention_scores, 0), 1]), antecedent_scores], 1) # [num_mentions, max_ant + 1]
return antecedent_scores, pair_emb # [num_mentions, max_ant + 1]
def get_antecedent_scores(self, mention_emb, mention_scores, antecedents, antecedents_len, mention_starts, mention_ends, mention_speaker_ids, genre_emb):
num_mentions = util.shape(mention_emb, 0)
max_antecedents = util.shape(antecedents, 1)
feature_emb_list = []
if self.config["use_metadata"]:
antecedent_speaker_ids = tf.gather(mention_speaker_ids, antecedents) # [num_mentions, max_ant]
same_speaker = tf.equal(tf.expand_dims(mention_speaker_ids, 1), antecedent_speaker_ids) # [num_mentions, max_ant]
speaker_pair_emb = tf.gather(tf.get_variable("same_speaker_emb", [2, self.config["feature_size"]]), tf.to_int32(same_speaker)) # [num_mentions, max_ant, emb]
feature_emb_list.append(speaker_pair_emb)
tiled_genre_emb = tf.tile(tf.expand_dims(tf.expand_dims(genre_emb, 0), 0), [num_mentions, max_antecedents, 1]) # [num_mentions, max_ant, emb]
feature_emb_list.append(tiled_genre_emb)
if self.config["use_features"]:
target_indices = tf.range(num_mentions) # [num_mentions]
mention_distance = tf.expand_dims(target_indices, 1) - antecedents # [num_mentions, max_ant]
mention_distance_bins = coref_ops.distance_bins(mention_distance) # [num_mentions, max_ant]
mention_distance_bins.set_shape([None, None])
mention_distance_emb = tf.gather(tf.get_variable("mention_distance_emb", [10, self.config["feature_size"]]), mention_distance_bins) # [num_mentions, max_ant]
feature_emb_list.append(mention_distance_emb)
feature_emb = tf.concat(feature_emb_list, 2) # [num_mentions, max_ant, emb]
feature_emb = tf.nn.dropout(feature_emb, self.dropout) # [num_mentions, max_ant, emb]
antecedent_emb = tf.gather(mention_emb, antecedents) # [num_mentions, max_ant, emb]
target_emb_tiled = tf.tile(tf.expand_dims(mention_emb, 1), [1, max_antecedents, 1]) # [num_mentions, max_ant, emb]
similarity_emb = antecedent_emb * target_emb_tiled # [num_mentions, max_ant, emb]
pair_emb = tf.concat([target_emb_tiled, antecedent_emb, similarity_emb, feature_emb], 2) # [num_mentions, max_ant, emb]
with tf.variable_scope("iteration"):
with tf.variable_scope("antecedent_scoring"):
antecedent_scores = util.ffnn(pair_emb, self.config["ffnn_depth"], self.config["ffnn_size"], 1, self.dropout) # [num_mentions, max_ant, 1]
antecedent_scores = tf.squeeze(antecedent_scores, 2) # [num_mentions, max_ant]
antecedent_mask = tf.log(tf.sequence_mask(antecedents_len, max_antecedents, dtype=tf.float32)) # [num_mentions, max_ant]
antecedent_scores += antecedent_mask # [num_mentions, max_ant]
antecedent_scores += tf.expand_dims(mention_scores, 1) + tf.gather(mention_scores, antecedents) # [num_mentions, max_ant]
antecedent_scores = tf.concat([tf.zeros([util.shape(mention_scores, 0), 1]), antecedent_scores], 1) # [num_mentions, max_ant + 1]
return antecedent_scores # [num_mentions, max_ant + 1]
def get_context_antecedent_scores(self,
mention_emb,
mention_scores,
antecedents,
antecedents_len,
mention_starts,
mention_ends,
mention_speaker_ids,
genre_emb,
context_starts,
context_ends,
text_outputs,
text_emb):
num_mentions = util.shape(mention_emb, 0)
max_antecedents = util.shape(antecedents, 1)
self.num_words = tf.shape(text_outputs)
self.num_mentions = num_mentions
feature_emb_list = []
if self.config["use_metadata"]:
antecedent_speaker_ids = tf.gather(mention_speaker_ids, antecedents) # [num_mentions, max_ant]
same_speaker = tf.equal(tf.expand_dims(mention_speaker_ids, 1), antecedent_speaker_ids) # [num_mentions, max_ant]
speaker_pair_emb = tf.gather(tf.get_variable("same_speaker_emb", [2, self.config["feature_size"]]), tf.to_int32(same_speaker)) # [num_mentions, max_ant, emb]
feature_emb_list.append(speaker_pair_emb)
tiled_genre_emb = tf.tile(tf.expand_dims(tf.expand_dims(genre_emb, 0), 0), [num_mentions, max_antecedents, 1]) # [num_mentions, max_ant, emb]
feature_emb_list.append(tiled_genre_emb)
if self.config["use_features"]:
target_indices = tf.range(num_mentions) # [num_mentions]
mention_distance = tf.expand_dims(target_indices, 1) - antecedents # [num_mentions, max_ant]
mention_distance_bins = coref_ops.distance_bins(mention_distance) # [num_mentions, max_ant]
mention_distance_bins.set_shape([None, None])
mention_distance_emb = tf.gather(tf.get_variable("mention_distance_emb", [10, self.config["feature_size"]]), mention_distance_bins) # [num_mentions, max_ant]
feature_emb_list.append(mention_distance_emb)
feature_emb = tf.concat(feature_emb_list, 2) # [num_mentions, max_ant, emb]
feature_emb = tf.nn.dropout(feature_emb, self.dropout) # [num_mentions, max_ant, emb]
#############################
#
# Get matrix for co-attention
#
#############################
####### Mention Level #######
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_features = tf.concat([mention_start_emb, mention_end_emb], 1)
mention_width = 1 + mention_ends - mention_starts # [num_mentions]
mention_indices = tf.expand_dims(tf.range(self.config["max_mention_width"]), 0) + tf.expand_dims(mention_starts, 1) # [num_mentions, max_mention_width]
mention_indices = tf.minimum(util.shape(text_outputs, 0) - 1, mention_indices) # [num_mentions, max_mention_width]
mention_mask = tf.expand_dims(tf.sequence_mask(mention_width, self.config["max_mention_width"], dtype=tf.float32), 2) # [num_mentions, max_mention_width, 1]
antecedent_indices = tf.gather(mention_indices, antecedents)
antecedent_mask = tf.gather(mention_mask, antecedents)
antecedent_indices_emb = tf.gather(text_outputs, antecedent_indices)
target_indices = tf.tile(tf.expand_dims(mention_indices, 1), [1, max_antecedents, 1])
target_mask = tf.tile(tf.expand_dims(mention_mask, 1), [1, max_antecedents, 1, 1])
target_indices_emb = tf.gather(text_outputs, target_indices)
####### Context Level #######
context_start_emb = tf.gather(text_outputs, context_starts)
context_end_emb = tf.gather(text_outputs, context_ends)
context_width = 1 + context_ends - context_starts
context_indices = tf.expand_dims(tf.range(self.config["max_context_width"]), 0) + tf.expand_dims(context_starts, 1) # [num_mentions, max_mention_width]
context_indices = tf.minimum(util.shape(text_outputs, 0) - 1, context_indices) # [num_mentions, max_mention_width]
context_mask = tf.expand_dims(tf.sequence_mask(context_width, self.config["max_context_width"], dtype=tf.float32), 2) # [num_mentions, max_mention_width, 1]
antecedent_context_indices = tf.gather(context_indices, antecedents)
antecedent_context_mask = tf.gather(context_mask, antecedents)
antecedent_context_indices_emb = tf.gather(text_outputs, antecedent_context_indices)
target_context_indices = tf.tile(tf.expand_dims(context_indices, 1), [1, max_antecedents, 1])
target_context_mask = tf.tile(tf.expand_dims(context_mask, 1), [1, max_antecedents, 1, 1])
target_context_indices_emb = tf.gather(text_outputs, target_context_indices)
#### Initial Embeddings #####
antecedent_emb = tf.gather(mention_emb, antecedents) # [num_mentions, max_ant, emb]
target_emb_tiled = tf.tile(tf.expand_dims(mention_emb, 1), [1, max_antecedents, 1]) # [num_mentions, max_ant, emb]
context_emb = tf.concat([context_start_emb, context_end_emb], 1)
antecedent_context_emb = tf.gather(context_emb, antecedents) # [num_mentions, max_ant, emb]
target_context_emb_tiled = tf.tile(tf.expand_dims(context_emb, 1), [1, max_antecedents, 1]) # [num_mentions, max_ant, emb]
similarity_emb = antecedent_emb * target_emb_tiled # [num_mentions, max_ant, emb]
#############################
#
# Calculate Co-attention
#
#############################
###### C_a Attention ########
window_emb = tf.concat([antecedent_emb, target_emb_tiled, target_context_emb_tiled], 2)
window_scores = util.projection_name(window_emb, 100, 'c_a_window')
window_scores = tf.tile(tf.expand_dims(window_scores, 2), [1, 1, self.config['max_context_width'], 1])
target_scores = util.projection_name(antecedent_context_indices_emb, 100, 'c_a_target')
temp_scores = util.projection_name(window_scores + target_scores, 1, 'att_score')
temp_att = tf.nn.softmax(temp_scores + tf.log(antecedent_context_mask), dim=2) # [num_mentions, max_mention_width, 1]
antecedent_context_emb = tf.reduce_sum(temp_att * tf.gather(text_emb, antecedent_context_indices), 2)
###### C_t Attention ########
window_emb = tf.concat([antecedent_emb, target_emb_tiled, antecedent_context_emb], 2)
window_scores = util.projection_name(window_emb, 100, 'c_t_window')
window_scores = tf.tile(tf.expand_dims(window_scores, 2), [1, 1, self.config['max_context_width'], 1])
target_scores = util.projection_name(target_context_indices_emb, 100, 'c_t_target')
temp_scores = util.projection_name(window_scores + target_scores, 1, 'att_score')
temp_att = tf.nn.softmax(temp_scores + tf.log(target_context_mask), dim=2) # [num_mentions, max_mention_width, 1]
target_context_emb_tiled = tf.reduce_sum(temp_att * tf.gather(text_emb, target_context_indices), 2)
###### M_t Attention ########
window_emb = tf.concat([antecedent_emb, antecedent_context_emb, target_context_emb_tiled], 2)
window_scores = util.projection_name(window_emb, 100, 'm_t_window')
window_scores = tf.tile(tf.expand_dims(window_scores, 2), [1, 1, self.config['max_mention_width'], 1])
target_scores = util.projection_name(target_indices_emb, 100, 'm_t_target')
temp_scores = util.projection_name(window_scores + target_scores, 1, 'att_score')
temp_att = tf.nn.softmax(temp_scores + tf.log(target_mask), dim=2) # [num_mentions, max_mention_width, 1]
target_emb_tiled = tf.reduce_sum(temp_att * tf.gather(text_emb, target_indices), 2)
###### M_a Attention ########
window_emb = tf.concat([target_emb_tiled, target_context_emb_tiled, antecedent_context_emb], 2)
window_scores = util.projection_name(window_emb, 100, 'm_a_window')
window_scores = tf.tile(tf.expand_dims(window_scores, 2), [1, 1, self.config['max_mention_width'], 1])
target_scores = util.projection_name(antecedent_indices_emb, 100, 'm_a_target')
temp_scores = util.projection_name(window_scores + target_scores, 1, 'att_score')
temp_att = tf.nn.softmax(temp_scores + tf.log(antecedent_mask), dim=2) # [num_mentions, max_mention_width, 1]
antecedent_emb = tf.reduce_sum(temp_att * tf.gather(text_emb, antecedent_indices), 2)
#############################
#
# Calculate Pair Embeddings
#
#############################
antecedent_feature = tf.gather(mention_features, antecedents) # [num_mentions, max_ant, emb]
target_feature = tf.tile(tf.expand_dims(mention_features, 1), [1, max_antecedents, 1]) # [num_mentions, max_ant, emb]
# similarity_emb = antecedent_emb * target_emb_tiled # [num_mentions, max_ant, emb]
# pair_emb = tf.concat([target_emb_tiled_1, antecedent_emb_1, similarity_emb, feature_emb], 2) # [num_mentions, max_ant, emb]
pair_emb = tf.concat([
target_feature,
target_emb_tiled,
antecedent_feature,
antecedent_emb,
antecedent_context_emb,
target_context_emb_tiled,
similarity_emb,
feature_emb], 2)
'''
pair_emb = tf.nn.relu(util.projection_name(target_emb_tiled, self.config['ffnn_size'], 'comp_mt') +\
util.projection_name(antecedent_emb, self.config['ffnn_size'], 'comp_ma') +\
util.projection_name(antecedent_context_emb_1, self.config['ffnn_size'], 'comp_ca') +\
util.projection_name(target_context_emb_tiled_1, self.config['ffnn_size'], 'comp_ct') +\
util.projection_name(similarity_emb, self.config['ffnn_size'], 'comp_sim') +\
util.projection_name(feature_emb, self.config['ffnn_size'], 'comp_feature'))
'''
#############################
with tf.variable_scope("iteration"):
with tf.variable_scope("antecedent_scoring"):
antecedent_scores = util.ffnn(pair_emb, self.config["ffnn_depth"], self.config["ffnn_size"], 1, self.dropout) # [num_mentions, max_ant, 1]
antecedent_scores = tf.squeeze(antecedent_scores, 2) # [num_mentions, max_ant]
antecedent_mask = tf.log(tf.sequence_mask(antecedents_len, max_antecedents, dtype=tf.float32)) # [num_mentions, max_ant]
antecedent_scores += antecedent_mask # [num_mentions, max_ant]
antecedent_scores += tf.expand_dims(mention_scores, 1) + tf.gather(mention_scores, antecedents) # [num_mentions, max_ant]
antecedent_scores = tf.concat([tf.zeros([util.shape(mention_scores, 0), 1]), antecedent_scores], 1) # [num_mentions, max_ant + 1]
return antecedent_scores # [num_mentions, max_ant + 1]
def get_antecedent_scores(self, mention_emb, mention_scores, antecedents,
antecedents_len, mention_starts, mention_ends,
mention_speaker_ids, genre_emb, k):
num_mentions = util.shape(mention_emb, 0)
max_antecedents = util.shape(antecedents, 1)
feature_emb_list = []
if self.config["use_metadata"]:
antecedent_speaker_ids = tf.gather(
mention_speaker_ids, antecedents) # [num_mentions, max_ant]
same_speaker = tf.equal(
tf.expand_dims(mention_speaker_ids, 1),
antecedent_speaker_ids) # [num_mentions, max_ant]
speaker_pair_emb = tf.gather(
tf.get_variable("same_speaker_emb",
[2, self.config["feature_size"]]),
tf.to_int32(same_speaker)) # [num_mentions, max_ant, emb]
feature_emb_list.append(speaker_pair_emb)
tiled_genre_emb = tf.tile(
tf.expand_dims(tf.expand_dims(genre_emb, 0), 0),
[num_mentions, max_antecedents, 1
]) # [num_mentions, max_ant, emb]
feature_emb_list.append(tiled_genre_emb)
if self.config["use_features"]:
target_indices = tf.range(num_mentions) # [num_mentions]
mention_distance = tf.expand_dims(
target_indices, 1) - antecedents # [num_mentions, max_ant]
mention_distance_bins = coref_ops.distance_bins(
mention_distance) # [num_mentions, max_ant]
mention_distance_bins.set_shape([None, None])
mention_distance_emb = tf.gather(
tf.get_variable("mention_distance_emb",
[10, self.config["feature_size"]]),
mention_distance_bins) # [num_mentions, max_ant]
feature_emb_list.append(mention_distance_emb)
feature_emb = tf.concat(feature_emb_list,
2) # [num_mentions, max_ant, emb]
feature_emb = tf.nn.dropout(
feature_emb, self.dropout) # [num_mentions, max_ant, emb]
span_emb = tf.expand_dims(mention_emb, 0)
antecedent_scores = self.rgcn_tagging(span_emb, mention_scores,
feature_emb,
k) # [1, num_words, 100] ?
self.scores = antecedent_scores
'''
antecedent_emb = tf.gather(mention_emb, antecedents) # [num_mentions, max_ant, emb]
self.mention_emb_shape = tf.shape(mention_emb)
self.mention_start_shape = tf.shape(antecedents)
target_emb_tiled = tf.tile(tf.expand_dims(mention_emb, 1), [1, max_antecedents, 1]) # [num_mentions, max_ant, emb]
similarity_emb = antecedent_emb * target_emb_tiled # [num_mentions, max_ant, emb]
pair_emb = tf.concat([target_emb_tiled, antecedent_emb, similarity_emb, feature_emb], 2) # [num_mentions, max_ant, emb]
with tf.variable_scope("iteration"):
with tf.variable_scope("antecedent_scoring"):
antecedent_scores = util.ffnn(pair_emb, self.config["ffnn_depth"], self.config["ffnn_size"], 1, self.dropout) # [num_mentions, max_ant, 1]
antecedent_scores = tf.squeeze(antecedent_scores, 2) # [num_mentions, max_ant]
antecedent_mask = tf.log(tf.sequence_mask(antecedents_len, max_antecedents, dtype=tf.float32)) # [num_mentions, max_ant]
antecedent_scores += antecedent_mask # [num_mentions, max_ant]
antecedent_scores += tf.expand_dims(mention_scores, 1) + tf.gather(mention_scores, antecedents) # [num_mentions, max_ant]
antecedent_scores = tf.concat([tf.zeros([util.shape(mention_scores, 0), 1]), antecedent_scores], 1) # [num_mentions, max_ant + 1]
'''
return antecedent_scores # [num_mentions, max_ant + 1]
def get_antecedent_scores(self, mention_emb, mention_scores, antecedents,
antecedents_len, mention_starts, mention_ends,
mention_speaker_ids, genre_emb, text_emb,
text_outputs, context_pre_starts,
context_pos_ends):
num_mentions = util.shape(mention_emb, 0)
max_antecedents = util.shape(antecedents, 1)
feature_emb_list = []
if self.config["use_metadata"]:
antecedent_speaker_ids = tf.gather(
mention_speaker_ids, antecedents) # [num_mentions, max_ant]
same_speaker = tf.equal(
tf.expand_dims(mention_speaker_ids, 1),
antecedent_speaker_ids) # [num_mentions, max_ant]
speaker_pair_emb = tf.gather(
tf.get_variable("same_speaker_emb",
[2, self.config["feature_size"]]),
tf.to_int32(same_speaker)) # [num_mentions, max_ant, emb]
feature_emb_list.append(speaker_pair_emb)
tiled_genre_emb = tf.tile(
tf.expand_dims(tf.expand_dims(genre_emb, 0), 0),
[num_mentions, max_antecedents, 1
]) # [num_mentions, max_ant, emb]
feature_emb_list.append(tiled_genre_emb)
if self.config["use_features"]:
target_indices = tf.range(num_mentions) # [num_mentions]
mention_distance = tf.expand_dims(
target_indices, 1) - antecedents # [num_mentions, max_ant]
mention_distance_bins = coref_ops.distance_bins(
mention_distance) # [num_mentions, max_ant]
mention_distance_bins.set_shape([None, None])
mention_distance_emb = tf.gather(
tf.get_variable("mention_distance_emb",
[10, self.config["feature_size"]]),
mention_distance_bins) # [num_mentions, max_ant]
feature_emb_list.append(mention_distance_emb)
feature_emb = tf.concat(feature_emb_list,
2) # [num_mentions, max_ant, emb]
feature_emb = tf.nn.dropout(
feature_emb, self.dropout) # [num_mentions, max_ant, emb]
########### Context Embeddings #################
context_pre_ends = mention_starts - 1
context_pos_starts = mention_ends + 1
context_pre_width = mention_starts - context_pre_starts
context_pos_width = context_pos_ends - mention_ends
context_start_emb = tf.gather(text_outputs, context_pre_starts)
context_end_emb = tf.gather(text_outputs, context_pos_ends)
context_output = tf.concat([context_start_emb, context_end_emb], 1)
context_output = tf.tile(tf.expand_dims(context_output, 1),
[1, self.config["max_context_width"], 1])
mention_output = tf.tile(tf.expand_dims(mention_emb, 1),
[1, self.config["max_context_width"], 1])
# context_width = 1 + context_ends - context_starts
context_pre_indices = tf.expand_dims(
tf.range(
self.config["max_context_width"] / 2), 0) + tf.expand_dims(
context_pre_starts, 1) # [num_mentions, max_mention_width]
context_pre_indices = tf.minimum(
util.shape(text_outputs, 0) - 1,
context_pre_indices) # [num_mentions, max_mention_width]
context_pre_mask = tf.expand_dims(
tf.sequence_mask(context_pre_width,
self.config["max_context_width"] / 2,
dtype=tf.float32),
2) # [num_mentions, max_mention_width, 1]
context_pos_indices = tf.expand_dims(
tf.range(
self.config["max_context_width"] / 2), 0) + tf.expand_dims(
context_pos_starts, 1) # [num_mentions, max_mention_width]
context_pos_indices = tf.minimum(
util.shape(text_outputs, 0) - 1,
context_pos_indices) # [num_mentions, max_mention_width]
context_pos_mask = tf.expand_dims(
tf.sequence_mask(context_pos_width,
self.config["max_context_width"] / 2,
dtype=tf.float32),
2) # [num_mentions, max_mention_width, 1]
context_indices = tf.concat([context_pre_indices, context_pos_indices],
1)
context_mask = tf.concat([context_pre_mask, context_pos_mask], 1)
context_glove_emb = tf.gather(text_emb, context_indices)
context_att_score = util.projection_name(
tf.concat([context_glove_emb, context_output, mention_output], 2),
1, "context_att")
context_attention = tf.nn.softmax(
context_att_score + tf.log(context_mask),
dim=1) # [num_mentions, max_mention_width, 1]
context_emb = tf.reduce_sum(context_attention * context_glove_emb,
1) # [num_mentions, emb]
mention_emb = tf.concat([context_emb, mention_emb], 1)
################################################
antecedent_emb = tf.gather(mention_emb,
antecedents) # [num_mentions, max_ant, emb]
self.mention_emb_shape = tf.shape(mention_emb)
self.mention_start_shape = tf.shape(antecedents)
target_emb_tiled = tf.tile(
tf.expand_dims(mention_emb, 1),
[1, max_antecedents, 1]) # [num_mentions, max_ant, emb]
similarity_emb = antecedent_emb * target_emb_tiled # [num_mentions, max_ant, emb]
pair_emb = tf.concat(
[target_emb_tiled, antecedent_emb, similarity_emb, feature_emb],
2) # [num_mentions, max_ant, emb]
with tf.variable_scope("iteration"):
with tf.variable_scope("antecedent_scoring"):
antecedent_scores = util.ffnn(
pair_emb, self.config["ffnn_depth"],
self.config["ffnn_size"], 1,
self.dropout) # [num_mentions, max_ant, 1]
antecedent_scores = tf.squeeze(antecedent_scores,
2) # [num_mentions, max_ant]
antecedent_mask = tf.log(
tf.sequence_mask(antecedents_len,
max_antecedents,
dtype=tf.float32)) # [num_mentions, max_ant]
antecedent_scores += antecedent_mask # [num_mentions, max_ant]
antecedent_scores += tf.expand_dims(mention_scores, 1) + tf.gather(
mention_scores, antecedents) # [num_mentions, max_ant]
antecedent_scores = tf.concat(
[tf.zeros([util.shape(mention_scores, 0), 1]), antecedent_scores],
1) # [num_mentions, max_ant + 1]
return antecedent_scores # [num_mentions, max_ant + 1]
