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        <namePart type="given">Lili</namePart>
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        <namePart type="given">Yaoyuan</namePart>
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    <abstract>The study on human-computer conversation systems is a hot research topic nowadays. One of the prevailing methods to build the system is using the generative Sequence-to-Sequence (Seq2Seq) model through neural networks. However, the standard Seq2Seq model is prone to generate trivial responses. In this paper, we aim to generate a more meaningful and informative reply when answering a given question. We propose an implicit content-introducing method which incorporates additional information into the Seq2Seq model in a flexible way. Specifically, we fuse the general decoding and the auxiliary cue word information through our proposed hierarchical gated fusion unit. Experiments on real-life data demonstrate that our model consistently outperforms a set of competitive baselines in terms of BLEU scores and human evaluation.</abstract>
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%0 Conference Proceedings
%T Towards Implicit Content-Introducing for Generative Short-Text Conversation Systems
%A Yao, Lili
%A Zhang, Yaoyuan
%A Feng, Yansong
%A Zhao, Dongyan
%A Yan, Rui
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F yao-etal-2017-towards
%X The study on human-computer conversation systems is a hot research topic nowadays. One of the prevailing methods to build the system is using the generative Sequence-to-Sequence (Seq2Seq) model through neural networks. However, the standard Seq2Seq model is prone to generate trivial responses. In this paper, we aim to generate a more meaningful and informative reply when answering a given question. We propose an implicit content-introducing method which incorporates additional information into the Seq2Seq model in a flexible way. Specifically, we fuse the general decoding and the auxiliary cue word information through our proposed hierarchical gated fusion unit. Experiments on real-life data demonstrate that our model consistently outperforms a set of competitive baselines in terms of BLEU scores and human evaluation.
%R 10.18653/v1/D17-1233
%U https://aclanthology.org/D17-1233
%U https://doi.org/10.18653/v1/D17-1233
%P 2190-2199

Markdown (Informal)

[Towards Implicit Content-Introducing for Generative Short-Text Conversation Systems](https://aclanthology.org/D17-1233) (Yao et al., EMNLP 2017)

• Towards Implicit Content-Introducing for Generative Short-Text Conversation Systems (Yao et al., EMNLP 2017)

ACL

• Lili Yao, Yaoyuan Zhang, Yansong Feng, Dongyan Zhao, and Rui Yan. 2017. Towards Implicit Content-Introducing for Generative Short-Text Conversation Systems. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2190–2199, Copenhagen, Denmark. Association for Computational Linguistics.