Emotion-Aware Chat Machine

The source code of the paper "Emotion-aware Chat Machine: Automatic Emotional Response Generation for Human-like Emotional Interaction" (Published on CIKM2019).

Dependencies

• Python 3.5
• Numpy
• Tensorflow 0.12

Quick Start

• 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.

The basic format of the sample data is:

[[[post, primary emotion, secondary emotion], [response, primary emotion, secondary emotion]], ...]

where emotion tag1/tag2 is generated by the BERT model which is used as a multi-label classifier. The training data of this classifier can be found on NLPCC2014 and NLPCC2013 website.

We provide an example of the Dev_Set in this repository, which has the same format as the Train_Set.

• Train

  python baseline.py --use_emb --use_autoEM --train_dir="train/EACM"

Note that "--use_imemory", "--use_ememory" are originally designed for ECM model, and we do not need that for EACM.

• Test

  python baseline.py --use_emb --use_autoEM --train_dir="train/EACM" --decode

Need to note that the testing files placed in "./train/test.post", and the model will automatically generate responses according to the testfile. After predicting the testfile, the model will go to interaction mode to wait for an input.

Paper

Please kindly cite our paper if this paper and the code are helpful.

@inproceedings{10.1145/3357384.3357937,
author = {Wei, Wei and Liu, Jiayi and Mao, Xianling and Guo, Guibing and Zhu, Feida and Zhou, Pan and Hu, Yuchong},
title = {Emotion-Aware Chat Machine: Automatic Emotional Response Generation for Human-like Emotional Interaction},
year = {2019},
pages = {1401–1410},
location = {Beijing, China},
series = {CIKM '19}
}

Acknowlegments

Thanks Hao Zhou for sharing the original code of ECM model , which is available here.

License

Apache License 2.0