This repo contains the implementation of the two papers:

Automatic dialogue generation with expressed emotions

Generating Responses Expressing Emotion in an Open-domain Dialogue System

The second paper is basically an extension of the first, it shows four more approaches to express specified emotions.

The following figure shows an overview of all the 7 models.

The code is originally written in PyTorch0.3 and Python3.6

This project is heavily relying on emotion classifier. In this code ,we use a very simple Bi-LSTM model. The performance would very but not too much depending what kinda of text classifier you are using.

CBET dataset can be accessed through this link. It is balanced in single labeled emotions and preprocessed.

To replicate the results in the paper, you need to follow the following instructions:

1. Firstly, train an emotion classifier using CBET dataset.

2. Download jiwei's dataset as in his github page, I made a code that converts his dataset from token IDs to actual tokens.

python jiwei_dataset.py

If you find our work is helpful, please consider citing one of the following papers.

@inproceedings{huang2018automatic,
  title={Automatic dialogue generation with expressed emotions},
  author={Huang, Chenyang and Zaiane, Osmar and Trabelsi, Amine and Dziri, Nouha},
  booktitle={Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)},
  volume={2},
  pages={49--54},
  year={2018}
}

@article{huang2018generating,
  title={Generating Responses Expressing Emotion in an Open-domain Dialogue System},
  author={Huang, Chenyang and Za{\"\i}ane, Osmar R},
  journal={arXiv preprint arXiv:1811.10990},
  year={2018}
}