/ecm

This project is a tensorflow implement of our work, ECM.

Primary LanguagePython

Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory

This project is a tensorflow implement of our work, ECM.

Dataset

Due to the copyright of the STC dataset, you can ask Lifeng Shang (lifengshang@gmail.com) for the STC dataset (Neural Responding Machine for Short-Text Conversation), and build the ESTC dataset follow the instruction in the Data Preparation Section of our paper, ECM.

For your convenience, we also recommand you implement your model using the nlpcc2017 dataset (http://aihuang.org:8000/p/challenge.html), which has more than 1 million Weibo post-response pairs with emotional labels.

Usage

  1. train
python baseline.py --use_emb --use_imemory --use_ememory

You can remove "--use_emb", "--use_imemory", "--use_ememory" to remove the embedding, internal memory, and external memory module respectively.

  1. test
python baseline.py --use_emb --use_imemory --use_ememory --decode

You can test and apply the ecm model using this command. Note: the input words should be splitted by ' ', for example, '我 很 喜欢 你 ！', or you can add the chinese text segmentation module in split() function.

Requirements

  • Python 2.7
  • Tensorflow 0.12
  • Numpy