Neural_Conversation_Models

================================= This implementation contains an extension of seq2seq tutorial for conversation models in Tensorflow:

1. Option to use Beam Search and Beam Size for decoding

2. Currently, it supports

   • Simple seq2seq models
   • Attention based seq2seq models

3. To get better results use beam search during decoding / inference

Examples of basic model can be found in this paper.

https://arxiv.org/abs/1702.05512

Prerequisites

• Python 2.7 or Python 3.3+
• NLTK
• TensorFlow

Data

Data accepted is in the tsv format where first component is the context and second is the reply

TSV format Ubuntu Dialog Data can be found here

example :-

1. What are you doing ? \t Writing seq2seq model .

Usage

To train a model with Ubuntu dataset:

$ python neural_conversation_model.py --train_dir ubuntu/ --en_vocab_size 60000 --size 512 --data_path ubuntu/train.tsv --dev_data ubuntu/valid.tsv  --vocab_path ubuntu/60k_vocan.en --attention

To test an existing model:

$ python neural_conversation_model.py --train_dir ubuntu/ --en_vocab_size 60000 --size 512 --data_path ubuntu/train.tsv --dev_data ubuntu/valid.tsv  --vocab_path ubuntu/60k_vocan.en --attention --decode --beam_search --beam_size 25

Todo

1. Add other state of art neural models.
2. Adding layer normalization( in progress )

https://github.com/pbhatia243/tf-layer-norm

Contact

Parminder Bhatia, parminder.bhatia243@gmail.com