Implementation of ESIM(Enhanced LSTM for Natural Language Inference)

esim.py

"""
Implementation of ESIM(Enhanced LSTM for Natural Language Inference)
https://arxiv.org/abs/1609.06038
"""
import numpy as np
from keras.layers import *
from keras.activations import softmax
from keras.models import Model

def StaticEmbedding(embedding_matrix):
    in_dim, out_dim = embedding_matrix.shape
    return Embedding(in_dim, out_dim, weights=[embedding_matrix], trainable=False)

def subtract(input_1, input_2):
    minus_input_2 = Lambda(lambda x: -x)(input_2)
    return add([input_1, minus_input_2])

def aggregate(input_1, input_2, num_dense=300, dropout_rate=0.5):
    feat1 = concatenate([GlobalAvgPool1D()(input_1), GlobalMaxPool1D()(input_1)])
    feat2 = concatenate([GlobalAvgPool1D()(input_2), GlobalMaxPool1D()(input_2)])
    x = concatenate([feat1, feat2])
    x = BatchNormalization()(x)
    x = Dense(num_dense, activation='relu')(x)
    x = BatchNormalization()(x)
    x = Dropout(dropout_rate)(x)
    x = Dense(num_dense, activation='relu')(x)
    x = BatchNormalization()(x)
    x = Dropout(dropout_rate)(x)
    return x    

def align(input_1, input_2):
    attention = Dot(axes=-1)([input_1, input_2])
    w_att_1 = Lambda(lambda x: softmax(x, axis=1))(attention)
    w_att_2 = Permute((2,1))(Lambda(lambda x: softmax(x, axis=2))(attention))
    in1_aligned = Dot(axes=1)([w_att_1, input_1])
    in2_aligned = Dot(axes=1)([w_att_2, input_2])
    return in1_aligned, in2_aligned

def build_model(embedding_matrix, num_class=1, max_length=30, lstm_dim=300):
    q1 = Input(shape=(max_length,))
    q2 = Input(shape=(max_length,))
    
    # Embedding
    embedding = StaticEmbedding(embedding_matrix)
    q1_embed = BatchNormalization(axis=2)(embedding(q1))
    q2_embed = BatchNormalization(axis=2)(embedding(q2))

    # Encoding
    encode = Bidirectional(LSTM(lstm_dim, return_sequences=True))
    q1_encoded = encode(q1_embed)
    q2_encoded = encode(q2_embed)
    
    # Alignment
    q1_aligned, q2_aligned = align(q1_encoded, q2_encoded)
    
    # Compare
    q1_combined = concatenate([q1_encoded, q2_aligned, subtract(q1_encoded, q2_aligned), multiply([q1_encoded, q2_aligned])])
    q2_combined = concatenate([q2_encoded, q1_aligned, subtract(q2_encoded, q1_aligned), multiply([q2_encoded, q1_aligned])]) 
    compare = Bidirectional(LSTM(lstm_dim, return_sequences=True))
    q1_compare = compare(q1_combined)
    q2_compare = compare(q2_combined)
    
    # Aggregate
    x = aggregate(q1_compare, q2_compare)
    x = Dense(num_class, activation='sigmoid')(x)
    
    return Model(inputs=[q1, q2], outputs=x)

Hi.
Are you sure about the accuracy of the attention code?
This code is accuracy 86.33 on the SNLI data.

Please check the attention code?