@inproceedings{233e0b6e97044df6a65574681a03b8ca,
title = "TERG: Topic-Aware Emotional Response Generation for Chatbot",
abstract = "A more intelligent chatbot should be able to express emotion, in addition to providing informative responses. Despite much works in designing neural dialogue generation systems in recent years, few studies consider both emotion to be expressed and topic relevance in the generation process. To address this problem, we present a Topic-aware Emotional Response Generation (TERG) model, which can not only exactly generate desired emotional response but perform well in topic relevance. Specifically, TERG equips an encoder-decoder structure with an emotion aware module to control the emotional sentence generation and a topic aware module to enhance topic relevance. We evaluate our model on a large real-world dataset of conversations from social media. Experimental results show that our model obtains a significant improvement against several strong baseline methods on both automatic and human evaluation.",
keywords = "CVAE, Seq2Seq, dialogue generation, emotion, latent variable, topic aware commonsense",
author = "Pei Huo and Yan Yang and Jie Zhou and Chengcai Chen and Liang He",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 International Joint Conference on Neural Networks, IJCNN 2020 ; Conference date: 19-07-2020 Through 24-07-2020",
year = "2020",
month = jul,
doi = "10.1109/IJCNN48605.2020.9206719",
language = "英语",
series = "Proceedings of the International Joint Conference on Neural Networks",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings",
address = "美国",
}