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        <title>Relevant and Informative Response Generation using Pointwise Mutual Information</title>
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            <title>Proceedings of the First Workshop on NLP for Conversational AI</title>
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            <namePart type="given">Yun-Nung</namePart>
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            <namePart type="given">Dilek</namePart>
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            <namePart type="given">Thang-Minh</namePart>
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    <abstract>A sequence-to-sequence model tends to generate generic responses with little information for input utterances. To solve this problem, we propose a neural model that generates relevant and informative responses. Our model has simple architecture to enable easy application to existing neural dialogue models. Specifically, using positive pointwise mutual information, it first identifies keywords that frequently co-occur in responses given an utterance. Then, the model encourages the decoder to use the keywords for response generation. Experiment results demonstrate that our model successfully diversifies responses relative to previous models.</abstract>
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%0 Conference Proceedings
%T Relevant and Informative Response Generation using Pointwise Mutual Information
%A Takayama, Junya
%A Arase, Yuki
%Y Chen, Yun-Nung
%Y Bedrax-Weiss, Tania
%Y Hakkani-Tur, Dilek
%Y Kumar, Anuj
%Y Lewis, Mike
%Y Luong, Thang-Minh
%Y Su, Pei-Hao
%Y Wen, Tsung-Hsien
%S Proceedings of the First Workshop on NLP for Conversational AI
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F takayama-arase-2019-relevant
%X A sequence-to-sequence model tends to generate generic responses with little information for input utterances. To solve this problem, we propose a neural model that generates relevant and informative responses. Our model has simple architecture to enable easy application to existing neural dialogue models. Specifically, using positive pointwise mutual information, it first identifies keywords that frequently co-occur in responses given an utterance. Then, the model encourages the decoder to use the keywords for response generation. Experiment results demonstrate that our model successfully diversifies responses relative to previous models.
%R 10.18653/v1/W19-4115
%U https://aclanthology.org/W19-4115
%U https://doi.org/10.18653/v1/W19-4115
%P 133-138

Markdown (Informal)

[Relevant and Informative Response Generation using Pointwise Mutual Information](https://aclanthology.org/W19-4115) (Takayama & Arase, ACL 2019)

• Relevant and Informative Response Generation using Pointwise Mutual Information (Takayama & Arase, ACL 2019)

ACL

• Junya Takayama and Yuki Arase. 2019. Relevant and Informative Response Generation using Pointwise Mutual Information. In Proceedings of the First Workshop on NLP for Conversational AI, pages 133–138, Florence, Italy. Association for Computational Linguistics.