@inproceedings{zhang-etal-2020-topic,
title = "Topic-relevant Response Generation using Optimal Transport for an Open-domain Dialog System",
author = "Zhang, Shuying and
Zhao, Tianyu and
Kawahara, Tatsuya",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.359",
doi = "10.18653/v1/2020.coling-main.359",
pages = "4067--4077",
abstract = "Conventional neural generative models tend to generate safe and generic responses which have little connection with previous utterances semantically and would disengage users in a dialog system. To generate relevant responses, we propose a method that employs two types of constraints - topical constraint and semantic constraint. Under the hypothesis that a response and its context have higher relevance when they share the same topics, the topical constraint encourages the topics of a response to match its context by conditioning response decoding on topic words{'} embeddings. The semantic constraint, which encourages a response to be semantically related to its context by regularizing the decoding objective function with semantic distance, is proposed. Optimal transport is applied to compute a weighted semantic distance between the representation of a response and the context. Generated responses are evaluated by automatic metrics, as well as human judgment, showing that the proposed method can generate more topic-relevant and content-rich responses than conventional models.",
}
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%0 Conference Proceedings
%T Topic-relevant Response Generation using Optimal Transport for an Open-domain Dialog System
%A Zhang, Shuying
%A Zhao, Tianyu
%A Kawahara, Tatsuya
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F zhang-etal-2020-topic
%X Conventional neural generative models tend to generate safe and generic responses which have little connection with previous utterances semantically and would disengage users in a dialog system. To generate relevant responses, we propose a method that employs two types of constraints - topical constraint and semantic constraint. Under the hypothesis that a response and its context have higher relevance when they share the same topics, the topical constraint encourages the topics of a response to match its context by conditioning response decoding on topic words’ embeddings. The semantic constraint, which encourages a response to be semantically related to its context by regularizing the decoding objective function with semantic distance, is proposed. Optimal transport is applied to compute a weighted semantic distance between the representation of a response and the context. Generated responses are evaluated by automatic metrics, as well as human judgment, showing that the proposed method can generate more topic-relevant and content-rich responses than conventional models.
%R 10.18653/v1/2020.coling-main.359
%U https://aclanthology.org/2020.coling-main.359
%U https://doi.org/10.18653/v1/2020.coling-main.359
%P 4067-4077
Markdown (Informal)
[Topic-relevant Response Generation using Optimal Transport for an Open-domain Dialog System](https://aclanthology.org/2020.coling-main.359) (Zhang et al., COLING 2020)
ACL