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        <title>Sequence to Backward and Forward Sequences: A Content-Introducing Approach to Generative Short-Text Conversation</title>
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            <namePart type="given">Yuji</namePart>
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    <abstract>Using neural networks to generate replies in human-computer dialogue systems is attracting increasing attention over the past few years. However, the performance is not satisfactory: the neural network tends to generate safe, universally relevant replies which carry little meaning. In this paper, we propose a content-introducing approach to neural network-based generative dialogue systems. We first use pointwise mutual information (PMI) to predict a noun as a keyword, reflecting the main gist of the reply. We then propose seq2BF, a “sequence to backward and forward sequences” model, which generates a reply containing the given keyword. Experimental results show that our approach significantly outperforms traditional sequence-to-sequence models in terms of human evaluation and the entropy measure, and that the predicted keyword can appear at an appropriate position in the reply.</abstract>
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%0 Conference Proceedings
%T Sequence to Backward and Forward Sequences: A Content-Introducing Approach to Generative Short-Text Conversation
%A Mou, Lili
%A Song, Yiping
%A Yan, Rui
%A Li, Ge
%A Zhang, Lu
%A Jin, Zhi
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F mou-etal-2016-sequence
%X Using neural networks to generate replies in human-computer dialogue systems is attracting increasing attention over the past few years. However, the performance is not satisfactory: the neural network tends to generate safe, universally relevant replies which carry little meaning. In this paper, we propose a content-introducing approach to neural network-based generative dialogue systems. We first use pointwise mutual information (PMI) to predict a noun as a keyword, reflecting the main gist of the reply. We then propose seq2BF, a “sequence to backward and forward sequences” model, which generates a reply containing the given keyword. Experimental results show that our approach significantly outperforms traditional sequence-to-sequence models in terms of human evaluation and the entropy measure, and that the predicted keyword can appear at an appropriate position in the reply.
%U https://aclanthology.org/C16-1316
%P 3349-3358

Markdown (Informal)

[Sequence to Backward and Forward Sequences: A Content-Introducing Approach to Generative Short-Text Conversation](https://aclanthology.org/C16-1316) (Mou et al., COLING 2016)

• Sequence to Backward and Forward Sequences: A Content-Introducing Approach to Generative Short-Text Conversation (Mou et al., COLING 2016)

ACL

• Lili Mou, Yiping Song, Rui Yan, Ge Li, Lu Zhang, and Zhi Jin. 2016. Sequence to Backward and Forward Sequences: A Content-Introducing Approach to Generative Short-Text Conversation. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 3349–3358, Osaka, Japan. The COLING 2016 Organizing Committee.