@inproceedings{gao-etal-2019-discrete,
title = "A Discrete {CVAE} for Response Generation on Short-Text Conversation",
author = "Gao, Jun and
Bi, Wei and
Liu, Xiaojiang and
Li, Junhui and
Zhou, Guodong and
Shi, Shuming",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1198/",
doi = "10.18653/v1/D19-1198",
pages = "1898--1908",
abstract = "Neural conversation models such as encoder-decoder models are easy to generate bland and generic responses. Some researchers propose to use the conditional variational autoencoder (CVAE) which maximizes the lower bound on the conditional log-likelihood on a continuous latent variable. With different sampled latent variables, the model is expected to generate diverse responses. Although the CVAE-based models have shown tremendous potential, their improvement of generating high-quality responses is still unsatisfactory. In this paper, we introduce a discrete latent variable with an explicit semantic meaning to improve the CVAE on short-text conversation. A major advantage of our model is that we can exploit the semantic distance between the latent variables to maintain good diversity between the sampled latent variables. Accordingly, we propose a two-stage sampling approach to enable efficient diverse variable selection from a large latent space assumed in the short-text conversation task. Experimental results indicate that our model outperforms various kinds of generation models under both automatic and human evaluations and generates more diverse and informative responses."
}
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%0 Conference Proceedings
%T A Discrete CVAE for Response Generation on Short-Text Conversation
%A Gao, Jun
%A Bi, Wei
%A Liu, Xiaojiang
%A Li, Junhui
%A Zhou, Guodong
%A Shi, Shuming
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F gao-etal-2019-discrete
%X Neural conversation models such as encoder-decoder models are easy to generate bland and generic responses. Some researchers propose to use the conditional variational autoencoder (CVAE) which maximizes the lower bound on the conditional log-likelihood on a continuous latent variable. With different sampled latent variables, the model is expected to generate diverse responses. Although the CVAE-based models have shown tremendous potential, their improvement of generating high-quality responses is still unsatisfactory. In this paper, we introduce a discrete latent variable with an explicit semantic meaning to improve the CVAE on short-text conversation. A major advantage of our model is that we can exploit the semantic distance between the latent variables to maintain good diversity between the sampled latent variables. Accordingly, we propose a two-stage sampling approach to enable efficient diverse variable selection from a large latent space assumed in the short-text conversation task. Experimental results indicate that our model outperforms various kinds of generation models under both automatic and human evaluations and generates more diverse and informative responses.
%R 10.18653/v1/D19-1198
%U https://aclanthology.org/D19-1198/
%U https://doi.org/10.18653/v1/D19-1198
%P 1898-1908
Markdown (Informal)
[A Discrete CVAE for Response Generation on Short-Text Conversation](https://aclanthology.org/D19-1198/) (Gao et al., EMNLP-IJCNLP 2019)
ACL
- Jun Gao, Wei Bi, Xiaojiang Liu, Junhui Li, Guodong Zhou, and Shuming Shi. 2019. A Discrete CVAE for Response Generation on Short-Text Conversation. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 1898–1908, Hong Kong, China. Association for Computational Linguistics.