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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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    <abstract>A neural conversation model is a promising approach to develop dialogue systems with the ability of chit-chat. It allows training a model in an end-to-end manner without complex rule design nor feature engineering. However, as a side effect, the neural model tends to generate safe but uninformative and insensitive responses like “OK” and “I don’t know.” Such replies are called generic responses and regarded as a critical problem for user-engagement of dialogue systems. For a more engaging chit-chat experience, we propose a neural conversation model that generates responsive and self-expressive replies. Specifically, our model generates domain-aware and sentiment-rich responses. Experiments empirically confirmed that our model outperformed the sequence-to-sequence model; 68.1% of our responses were domain-aware with sentiment polarities, which was only 2.7% for responses generated by the sequence-to-sequence model.</abstract>
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%0 Conference Proceedings
%T Responsive and Self-Expressive Dialogue Generation
%A Chikai, Kozo
%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 chikai-etal-2019-responsive
%X A neural conversation model is a promising approach to develop dialogue systems with the ability of chit-chat. It allows training a model in an end-to-end manner without complex rule design nor feature engineering. However, as a side effect, the neural model tends to generate safe but uninformative and insensitive responses like “OK” and “I don’t know.” Such replies are called generic responses and regarded as a critical problem for user-engagement of dialogue systems. For a more engaging chit-chat experience, we propose a neural conversation model that generates responsive and self-expressive replies. Specifically, our model generates domain-aware and sentiment-rich responses. Experiments empirically confirmed that our model outperformed the sequence-to-sequence model; 68.1% of our responses were domain-aware with sentiment polarities, which was only 2.7% for responses generated by the sequence-to-sequence model.
%R 10.18653/v1/W19-4116
%U https://aclanthology.org/W19-4116
%U https://doi.org/10.18653/v1/W19-4116
%P 139-149

Markdown (Informal)

[Responsive and Self-Expressive Dialogue Generation](https://aclanthology.org/W19-4116) (Chikai et al., ACL 2019)

• Responsive and Self-Expressive Dialogue Generation (Chikai et al., ACL 2019)

ACL

• Kozo Chikai, Junya Takayama, and Yuki Arase. 2019. Responsive and Self-Expressive Dialogue Generation. In Proceedings of the First Workshop on NLP for Conversational AI, pages 139–149, Florence, Italy. Association for Computational Linguistics.