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        <title>Learning Neural Templates for Text Generation</title>
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    <name type="personal">
        <namePart type="given">Sam</namePart>
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        <namePart type="given">Stuart</namePart>
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    <name type="personal">
        <namePart type="given">Alexander</namePart>
        <namePart type="family">Rush</namePart>
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        <dateIssued>2018-oct-nov</dateIssued>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Ellen</namePart>
            <namePart type="family">Riloff</namePart>
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        <name type="personal">
            <namePart type="given">David</namePart>
            <namePart type="family">Chiang</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Julia</namePart>
            <namePart type="family">Hockenmaier</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Jun’ichi</namePart>
            <namePart type="family">Tsujii</namePart>
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    <abstract>While neural, encoder-decoder models have had significant empirical success in text generation, there remain several unaddressed problems with this style of generation. Encoder-decoder models are largely (a) uninterpretable, and (b) difficult to control in terms of their phrasing or content. This work proposes a neural generation system using a hidden semi-markov model (HSMM) decoder, which learns latent, discrete templates jointly with learning to generate. We show that this model learns useful templates, and that these templates make generation both more interpretable and controllable. Furthermore, we show that this approach scales to real data sets and achieves strong performance nearing that of encoder-decoder text generation models.</abstract>
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    <identifier type="doi">10.18653/v1/D18-1356</identifier>
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        <date>2018-oct-nov</date>
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%0 Conference Proceedings
%T Learning Neural Templates for Text Generation
%A Wiseman, Sam
%A Shieber, Stuart
%A Rush, Alexander
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F wiseman-etal-2018-learning
%X While neural, encoder-decoder models have had significant empirical success in text generation, there remain several unaddressed problems with this style of generation. Encoder-decoder models are largely (a) uninterpretable, and (b) difficult to control in terms of their phrasing or content. This work proposes a neural generation system using a hidden semi-markov model (HSMM) decoder, which learns latent, discrete templates jointly with learning to generate. We show that this model learns useful templates, and that these templates make generation both more interpretable and controllable. Furthermore, we show that this approach scales to real data sets and achieves strong performance nearing that of encoder-decoder text generation models.
%R 10.18653/v1/D18-1356
%U https://aclanthology.org/D18-1356
%U https://doi.org/10.18653/v1/D18-1356
%P 3174-3187

Markdown (Informal)

[Learning Neural Templates for Text Generation](https://aclanthology.org/D18-1356) (Wiseman et al., EMNLP 2018)

• Learning Neural Templates for Text Generation (Wiseman et al., EMNLP 2018)

ACL

• Sam Wiseman, Stuart Shieber, and Alexander Rush. 2018. Learning Neural Templates for Text Generation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3174–3187, Brussels, Belgium. Association for Computational Linguistics.