ARCII-for-Matching-Natural-Language-Sentences

A simple version of ARC-II model implemented in Keras.
Please reference paper：Convolutional Neural Network Architectures for Matching Natural Language Sentences

Quick Glance

1. Input Data Format

• Train set:

label	|q1	|q2
1	|Amrozi accused his brother, whom he called "the witness", of deliberately distorting his evidence.	|Referring to him as only "the witness", Amrozi accused his brother of deliberately distorting his evidence.
0	|Yucaipa owned Dominick's before selling the chain to Safeway in 1998 for $2.5 billion.	|Yucaipa bought Dominick's in 1995 for $693 million and sold it to Safeway for $1.8 billion in 1998.

• Test set:

q1	|q2
Amrozi accused his brother, whom he called "the witness", of deliberately distorting his evidence.	|Referring to him as only "the witness", Amrozi accused his brother of deliberately distorting his evidence.
Yucaipa owned Dominick's before selling the chain to Safeway in 1998 for $2.5 billion.	|Yucaipa bought Dominick's in 1995 for $693 million and sold it to Safeway for $1.8 billion in 1998.

• Word Embedding:

word	|embedding (300-dimension)
Amrozi	|-0.54645991 2.28509140 ... -0.34052843 -2.01874685
chief	|-9.01635551 -3.80108356 ... 1.86873138 2.14706421

2. Train the model

$ python arcii.py

3. Loss and Accuracy
   A toy data set example copied from MatchZoo's toy example

Requirements

• Python 3.5
• TensorFlow 1.8.0
• Keras 2.1.6

To do list

• Negative Sampling
  
• Mask zero inputs