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    <titleInfo>
        <title>Learning to Abstract for Memory-augmented Conversational Response Generation</title>
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        <namePart type="given">Zhiliang</namePart>
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        <namePart type="given">Nevin</namePart>
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    <abstract>Neural generative models for open-domain chit-chat conversations have become an active area of research in recent years. A critical issue with most existing generative models is that the generated responses lack informativeness and diversity. A few researchers attempt to leverage the results of retrieval models to strengthen the generative models, but these models are limited by the quality of the retrieval results. In this work, we propose a memory-augmented generative model, which learns to abstract from the training corpus and saves the useful information to the memory to assist the response generation. Our model clusters query-response samples, extracts characteristics of each cluster, and learns to utilize these characteristics for response generation. Experimental results show that our model outperforms other competitive baselines.</abstract>
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    <identifier type="doi">10.18653/v1/P19-1371</identifier>
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        <url>https://aclanthology.org/P19-1371</url>
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        <date>2019-07</date>
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%0 Conference Proceedings
%T Learning to Abstract for Memory-augmented Conversational Response Generation
%A Tian, Zhiliang
%A Bi, Wei
%A Li, Xiaopeng
%A Zhang, Nevin L.
%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 tian-etal-2019-learning
%X Neural generative models for open-domain chit-chat conversations have become an active area of research in recent years. A critical issue with most existing generative models is that the generated responses lack informativeness and diversity. A few researchers attempt to leverage the results of retrieval models to strengthen the generative models, but these models are limited by the quality of the retrieval results. In this work, we propose a memory-augmented generative model, which learns to abstract from the training corpus and saves the useful information to the memory to assist the response generation. Our model clusters query-response samples, extracts characteristics of each cluster, and learns to utilize these characteristics for response generation. Experimental results show that our model outperforms other competitive baselines.
%R 10.18653/v1/P19-1371
%U https://aclanthology.org/P19-1371
%U https://doi.org/10.18653/v1/P19-1371
%P 3816-3825

Markdown (Informal)

[Learning to Abstract for Memory-augmented Conversational Response Generation](https://aclanthology.org/P19-1371) (Tian et al., ACL 2019)

• Learning to Abstract for Memory-augmented Conversational Response Generation (Tian et al., ACL 2019)

ACL

• Zhiliang Tian, Wei Bi, Xiaopeng Li, and Nevin L. Zhang. 2019. Learning to Abstract for Memory-augmented Conversational Response Generation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3816–3825, Florence, Italy. Association for Computational Linguistics.