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        <title>HL-EncDec: A Hybrid-Level Encoder-Decoder for Neural Response Generation</title>
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    <name type="personal">
        <namePart type="given">Sixing</namePart>
        <namePart type="family">Wu</namePart>
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        <namePart type="given">Dawei</namePart>
        <namePart type="family">Zhang</namePart>
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    <name type="personal">
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    <name type="personal">
        <namePart type="given">Xing</namePart>
        <namePart type="family">Xie</namePart>
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    <name type="personal">
        <namePart type="given">Zhonghai</namePart>
        <namePart type="family">Wu</namePart>
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            <title>Proceedings of the 27th International Conference on Computational Linguistics</title>
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            <namePart type="given">Emily</namePart>
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    <abstract>Recent years have witnessed a surge of interest on response generation for neural conversation systems. Most existing models are implemented by following the Encoder-Decoder framework and operate sentences of conversations at word-level. The word-level model is suffering from the Unknown Words Issue and the Preference Issue, which seriously impact the quality of generated responses, for example, generated responses may become irrelevant or too general (i.e. safe responses). To address these issues, this paper proposes a hybrid-level Encoder-Decoder model (HL-EncDec), which not only utilizes the word-level features but also character-level features. We conduct several experiments to evaluate HL-EncDec on a Chinese corpus, experimental results show our model significantly outperforms other non-word-level models in automatic metrics and human annotations and is able to generate more informative responses. We also conduct experiments with a small-scale English dataset to show the generalization ability.</abstract>
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        <url>https://aclanthology.org/C18-1072</url>
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        <date>2018-08</date>
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%0 Conference Proceedings
%T HL-EncDec: A Hybrid-Level Encoder-Decoder for Neural Response Generation
%A Wu, Sixing
%A Zhang, Dawei
%A Li, Ying
%A Xie, Xing
%A Wu, Zhonghai
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F wu-etal-2018-hl
%X Recent years have witnessed a surge of interest on response generation for neural conversation systems. Most existing models are implemented by following the Encoder-Decoder framework and operate sentences of conversations at word-level. The word-level model is suffering from the Unknown Words Issue and the Preference Issue, which seriously impact the quality of generated responses, for example, generated responses may become irrelevant or too general (i.e. safe responses). To address these issues, this paper proposes a hybrid-level Encoder-Decoder model (HL-EncDec), which not only utilizes the word-level features but also character-level features. We conduct several experiments to evaluate HL-EncDec on a Chinese corpus, experimental results show our model significantly outperforms other non-word-level models in automatic metrics and human annotations and is able to generate more informative responses. We also conduct experiments with a small-scale English dataset to show the generalization ability.
%U https://aclanthology.org/C18-1072
%P 845-856

Markdown (Informal)

[HL-EncDec: A Hybrid-Level Encoder-Decoder for Neural Response Generation](https://aclanthology.org/C18-1072) (Wu et al., COLING 2018)

• HL-EncDec: A Hybrid-Level Encoder-Decoder for Neural Response Generation (Wu et al., COLING 2018)

ACL

• Sixing Wu, Dawei Zhang, Ying Li, Xing Xie, and Zhonghai Wu. 2018. HL-EncDec: A Hybrid-Level Encoder-Decoder for Neural Response Generation. In Proceedings of the 27th International Conference on Computational Linguistics, pages 845–856, Santa Fe, New Mexico, USA. Association for Computational Linguistics.