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        <title>Retrieval-Enhanced Adversarial Training for Neural Response Generation</title>
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        <namePart type="given">Qingfu</namePart>
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    <abstract>Dialogue systems are usually built on either generation-based or retrieval-based approaches, yet they do not benefit from the advantages of different models. In this paper, we propose a Retrieval-Enhanced Adversarial Training (REAT) method for neural response generation. Distinct from existing approaches, the REAT method leverages an encoder-decoder framework in terms of an adversarial training paradigm, while taking advantage of N-best response candidates from a retrieval-based system to construct the discriminator. An empirical study on a large scale public available benchmark dataset shows that the REAT method significantly outperforms the vanilla Seq2Seq model as well as the conventional adversarial training approach.</abstract>
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%0 Conference Proceedings
%T Retrieval-Enhanced Adversarial Training for Neural Response Generation
%A Zhu, Qingfu
%A Cui, Lei
%A Zhang, Wei-Nan
%A Wei, Furu
%A Liu, Ting
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F zhu-etal-2019-retrieval
%X Dialogue systems are usually built on either generation-based or retrieval-based approaches, yet they do not benefit from the advantages of different models. In this paper, we propose a Retrieval-Enhanced Adversarial Training (REAT) method for neural response generation. Distinct from existing approaches, the REAT method leverages an encoder-decoder framework in terms of an adversarial training paradigm, while taking advantage of N-best response candidates from a retrieval-based system to construct the discriminator. An empirical study on a large scale public available benchmark dataset shows that the REAT method significantly outperforms the vanilla Seq2Seq model as well as the conventional adversarial training approach.
%R 10.18653/v1/P19-1366
%U https://aclanthology.org/P19-1366
%U https://doi.org/10.18653/v1/P19-1366
%P 3763-3773

Markdown (Informal)

[Retrieval-Enhanced Adversarial Training for Neural Response Generation](https://aclanthology.org/P19-1366) (Zhu et al., ACL 2019)

• Retrieval-Enhanced Adversarial Training for Neural Response Generation (Zhu et al., ACL 2019)

ACL

• Qingfu Zhu, Lei Cui, Wei-Nan Zhang, Furu Wei, and Ting Liu. 2019. Retrieval-Enhanced Adversarial Training for Neural Response Generation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3763–3773, Florence, Italy. Association for Computational Linguistics.