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        <title>Learning to Ask Questions in Open-domain Conversational Systems with Typed Decoders</title>
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    <name type="personal">
        <namePart type="given">Liqiang</namePart>
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            <namePart type="given">Yusuke</namePart>
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    <abstract>Asking good questions in open-domain conversational systems is quite significant but rather untouched. This task, substantially different from traditional question generation, requires to question not only with various patterns but also on diverse and relevant topics. We observe that a good question is a natural composition of interrogatives, topic words, and ordinary words. Interrogatives lexicalize the pattern of questioning, topic words address the key information for topic transition in dialogue, and ordinary words play syntactical and grammatical roles in making a natural sentence. We devise two typed decoders (soft typed decoder and hard typed decoder) in which a type distribution over the three types is estimated and the type distribution is used to modulate the final generation distribution. Extensive experiments show that the typed decoders outperform state-of-the-art baselines and can generate more meaningful questions.</abstract>
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    <identifier type="doi">10.18653/v1/P18-1204</identifier>
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        <date>2018-07</date>
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            <start>2193</start>
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%0 Conference Proceedings
%T Learning to Ask Questions in Open-domain Conversational Systems with Typed Decoders
%A Wang, Yansen
%A Liu, Chenyi
%A Huang, Minlie
%A Nie, Liqiang
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F wang-etal-2018-learning-ask
%X Asking good questions in open-domain conversational systems is quite significant but rather untouched. This task, substantially different from traditional question generation, requires to question not only with various patterns but also on diverse and relevant topics. We observe that a good question is a natural composition of interrogatives, topic words, and ordinary words. Interrogatives lexicalize the pattern of questioning, topic words address the key information for topic transition in dialogue, and ordinary words play syntactical and grammatical roles in making a natural sentence. We devise two typed decoders (soft typed decoder and hard typed decoder) in which a type distribution over the three types is estimated and the type distribution is used to modulate the final generation distribution. Extensive experiments show that the typed decoders outperform state-of-the-art baselines and can generate more meaningful questions.
%R 10.18653/v1/P18-1204
%U https://aclanthology.org/P18-1204
%U https://doi.org/10.18653/v1/P18-1204
%P 2193-2203

Markdown (Informal)

[Learning to Ask Questions in Open-domain Conversational Systems with Typed Decoders](https://aclanthology.org/P18-1204) (Wang et al., ACL 2018)

• Learning to Ask Questions in Open-domain Conversational Systems with Typed Decoders (Wang et al., ACL 2018)

ACL

• Yansen Wang, Chenyi Liu, Minlie Huang, and Liqiang Nie. 2018. Learning to Ask Questions in Open-domain Conversational Systems with Typed Decoders. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2193–2203, Melbourne, Australia. Association for Computational Linguistics.