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        <title>Group-wise Contrastive Learning for Neural Dialogue Generation</title>
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        <namePart type="given">Hengyi</namePart>
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        <namePart type="given">Hongshen</namePart>
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    <name type="personal">
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    <abstract>Neural dialogue response generation has gained much popularity in recent years. Maximum Likelihood Estimation (MLE) objective is widely adopted in existing dialogue model learning. However, models trained with MLE objective function are plagued by the low-diversity issue when it comes to the open-domain conversational setting. Inspired by the observation that humans not only learn from the positive signals but also benefit from correcting behaviors of undesirable actions, in this work, we introduce contrastive learning into dialogue generation, where the model explicitly perceives the difference between the well-chosen positive and negative utterances. Specifically, we employ a pretrained baseline model as a reference. During contrastive learning, the target dialogue model is trained to give higher conditional probabilities for the positive samples, and lower conditional probabilities for those negative samples, compared to the reference model. To manage the multi-mapping relations prevalent in human conversation, we augment contrastive dialogue learning with group-wise dual sampling. Extensive experimental results show that the proposed group-wise contrastive learning framework is suited for training a wide range of neural dialogue generation models with very favorable performance over the baseline training approaches.</abstract>
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    <identifier type="doi">10.18653/v1/2020.findings-emnlp.70</identifier>
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%0 Conference Proceedings
%T Group-wise Contrastive Learning for Neural Dialogue Generation
%A Cai, Hengyi
%A Chen, Hongshen
%A Song, Yonghao
%A Ding, Zhuoye
%A Bao, Yongjun
%A Yan, Weipeng
%A Zhao, Xiaofang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 nov
%I Association for Computational Linguistics
%C Online
%F cai-etal-2020-group
%X Neural dialogue response generation has gained much popularity in recent years. Maximum Likelihood Estimation (MLE) objective is widely adopted in existing dialogue model learning. However, models trained with MLE objective function are plagued by the low-diversity issue when it comes to the open-domain conversational setting. Inspired by the observation that humans not only learn from the positive signals but also benefit from correcting behaviors of undesirable actions, in this work, we introduce contrastive learning into dialogue generation, where the model explicitly perceives the difference between the well-chosen positive and negative utterances. Specifically, we employ a pretrained baseline model as a reference. During contrastive learning, the target dialogue model is trained to give higher conditional probabilities for the positive samples, and lower conditional probabilities for those negative samples, compared to the reference model. To manage the multi-mapping relations prevalent in human conversation, we augment contrastive dialogue learning with group-wise dual sampling. Extensive experimental results show that the proposed group-wise contrastive learning framework is suited for training a wide range of neural dialogue generation models with very favorable performance over the baseline training approaches.
%R 10.18653/v1/2020.findings-emnlp.70
%U https://aclanthology.org/2020.findings-emnlp.70
%U https://doi.org/10.18653/v1/2020.findings-emnlp.70
%P 793-802

Markdown (Informal)

[Group-wise Contrastive Learning for Neural Dialogue Generation](https://aclanthology.org/2020.findings-emnlp.70) (Cai et al., Findings 2020)

• Group-wise Contrastive Learning for Neural Dialogue Generation (Cai et al., Findings 2020)

ACL

• Hengyi Cai, Hongshen Chen, Yonghao Song, Zhuoye Ding, Yongjun Bao, Weipeng Yan, and Xiaofang Zhao. 2020. Group-wise Contrastive Learning for Neural Dialogue Generation. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 793–802, Online. Association for Computational Linguistics.