Code for reproducing main results in the paper A Neural Conversational Model to build a conversational chat bot using seq2seq model.

This model extends seq2seq with these features:

• Reinforcment learning for more coherent dialouge instead of 1-to-1 relpy.
• Buckets while training to limit memory consumption
• Beam search to avoid local minima resopnses (needs to be optimized)
• Anti-langauge moddel to supress global solutions like "Thanks, ok" when uncertain.

• python 3.5
• TensorFlow 1.0 version
• asyncio
• websockets

chmod +x setup
./setup

1. The format should be one txt file with input on a line followed by the response on the next line.

   Datasets/
   |
   |-Your-dataset/
     |
     |-data/raw/
       |- chat.txt
   
   twitter             Twitter chat by [Marsan](https://github.com/Marsan-Ma/chat_corpus)
   

2. Optional: to get trained model, download and extract:

   • twitter checkpoint

• To train Seq2Seq run seq2seq_train passing model name

  python seq2seq_train.py --model_name twitter and pass in params
  

• To evaluate Seq2Seq run seq2seq_eval passing model name

  python seq2seq_eval.py --model_name twitter and pass in params
  

• Or simply run ./twitter_chat which will download the trained model and start the chatting interface. This model wasn't trained with reinforcment learning as the chat dataset is not dialouge.

  ./twitter_chat
  

• Make sure rosbridge_websocket (port 9090) is running on the ROS master
• In order to start the ROS node and service for answer generation call

  ./advertise_gnlp_service
  

• Call a service

  rosservice call /roboy/cognition/generative_nlp/answer "text_input: 'hello'"
  

[1] Sequence to Sequence Learning with Neural Networks [2] A Neural Conversational Model