@inproceedings{su-etal-2020-diversifying,
title = "Diversifying Dialogue Generation with Non-Conversational Text",
author = "Su, Hui and
Shen, Xiaoyu and
Zhao, Sanqiang and
Xiao, Zhou and
Hu, Pengwei and
Zhong, Randy and
Niu, Cheng and
Zhou, Jie",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.634",
doi = "10.18653/v1/2020.acl-main.634",
pages = "7087--7097",
abstract = "Neural network-based sequence-to-sequence (seq2seq) models strongly suffer from the low-diversity problem when it comes to open-domain dialogue generation. As bland and generic utterances usually dominate the frequency distribution in our daily chitchat, avoiding them to generate more interesting responses requires complex data filtering, sampling techniques or modifying the training objective. In this paper, we propose a new perspective to diversify dialogue generation by leveraging \textit{non-conversational} text. Compared with bilateral conversations, non-conversational text are easier to obtain, more diverse and cover a much broader range of topics. We collect a large-scale non-conversational corpus from multi sources including forum comments, idioms and book snippets. We further present a training paradigm to effectively incorporate these text via iterative back translation. The resulting model is tested on two conversational datasets from different domains and is shown to produce significantly more diverse responses without sacrificing the relevance with context.",
}
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<abstract>Neural network-based sequence-to-sequence (seq2seq) models strongly suffer from the low-diversity problem when it comes to open-domain dialogue generation. As bland and generic utterances usually dominate the frequency distribution in our daily chitchat, avoiding them to generate more interesting responses requires complex data filtering, sampling techniques or modifying the training objective. In this paper, we propose a new perspective to diversify dialogue generation by leveraging non-conversational text. Compared with bilateral conversations, non-conversational text are easier to obtain, more diverse and cover a much broader range of topics. We collect a large-scale non-conversational corpus from multi sources including forum comments, idioms and book snippets. We further present a training paradigm to effectively incorporate these text via iterative back translation. The resulting model is tested on two conversational datasets from different domains and is shown to produce significantly more diverse responses without sacrificing the relevance with context.</abstract>
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%0 Conference Proceedings
%T Diversifying Dialogue Generation with Non-Conversational Text
%A Su, Hui
%A Shen, Xiaoyu
%A Zhao, Sanqiang
%A Xiao, Zhou
%A Hu, Pengwei
%A Zhong, Randy
%A Niu, Cheng
%A Zhou, Jie
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F su-etal-2020-diversifying
%X Neural network-based sequence-to-sequence (seq2seq) models strongly suffer from the low-diversity problem when it comes to open-domain dialogue generation. As bland and generic utterances usually dominate the frequency distribution in our daily chitchat, avoiding them to generate more interesting responses requires complex data filtering, sampling techniques or modifying the training objective. In this paper, we propose a new perspective to diversify dialogue generation by leveraging non-conversational text. Compared with bilateral conversations, non-conversational text are easier to obtain, more diverse and cover a much broader range of topics. We collect a large-scale non-conversational corpus from multi sources including forum comments, idioms and book snippets. We further present a training paradigm to effectively incorporate these text via iterative back translation. The resulting model is tested on two conversational datasets from different domains and is shown to produce significantly more diverse responses without sacrificing the relevance with context.
%R 10.18653/v1/2020.acl-main.634
%U https://aclanthology.org/2020.acl-main.634
%U https://doi.org/10.18653/v1/2020.acl-main.634
%P 7087-7097
Markdown (Informal)
[Diversifying Dialogue Generation with Non-Conversational Text](https://aclanthology.org/2020.acl-main.634) (Su et al., ACL 2020)
ACL
- Hui Su, Xiaoyu Shen, Sanqiang Zhao, Zhou Xiao, Pengwei Hu, Randy Zhong, Cheng Niu, and Jie Zhou. 2020. Diversifying Dialogue Generation with Non-Conversational Text. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7087–7097, Online. Association for Computational Linguistics.