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