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        <namePart type="given">Siqi</namePart>
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            <title>Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics</title>
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            <namePart type="given">Dan</namePart>
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    <abstract>Pre-training models have been proved effective for a wide range of natural language processing tasks. Inspired by this, we propose a novel dialogue generation pre-training framework to support various kinds of conversations, including chit-chat, knowledge grounded dialogues, and conversational question answering. In this framework, we adopt flexible attention mechanisms to fully leverage the bi-directional context and the uni-directional characteristic of language generation. We also introduce discrete latent variables to tackle the inherent one-to-many mapping problem in response generation. Two reciprocal tasks of response generation and latent act recognition are designed and carried out simultaneously within a shared network. Comprehensive experiments on three publicly available datasets verify the effectiveness and superiority of the proposed framework.</abstract>
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%0 Conference Proceedings
%T PLATO: Pre-trained Dialogue Generation Model with Discrete Latent Variable
%A Bao, Siqi
%A He, Huang
%A Wang, Fan
%A Wu, Hua
%A Wang, Haifeng
%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 bao-etal-2020-plato
%X Pre-training models have been proved effective for a wide range of natural language processing tasks. Inspired by this, we propose a novel dialogue generation pre-training framework to support various kinds of conversations, including chit-chat, knowledge grounded dialogues, and conversational question answering. In this framework, we adopt flexible attention mechanisms to fully leverage the bi-directional context and the uni-directional characteristic of language generation. We also introduce discrete latent variables to tackle the inherent one-to-many mapping problem in response generation. Two reciprocal tasks of response generation and latent act recognition are designed and carried out simultaneously within a shared network. Comprehensive experiments on three publicly available datasets verify the effectiveness and superiority of the proposed framework.
%R 10.18653/v1/2020.acl-main.9
%U https://aclanthology.org/2020.acl-main.9
%U https://doi.org/10.18653/v1/2020.acl-main.9
%P 85-96

Markdown (Informal)

[PLATO: Pre-trained Dialogue Generation Model with Discrete Latent Variable](https://aclanthology.org/2020.acl-main.9) (Bao et al., ACL 2020)

• PLATO: Pre-trained Dialogue Generation Model with Discrete Latent Variable (Bao et al., ACL 2020)

ACL

• Siqi Bao, Huang He, Fan Wang, Hua Wu, and Haifeng Wang. 2020. PLATO: Pre-trained Dialogue Generation Model with Discrete Latent Variable. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 85–96, Online. Association for Computational Linguistics.