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        <title>ViGGO: A Video Game Corpus for Data-To-Text Generation in Open-Domain Conversation</title>
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    <name type="personal">
        <namePart type="given">Juraj</namePart>
        <namePart type="family">Juraska</namePart>
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    <name type="personal">
        <namePart type="given">Kevin</namePart>
        <namePart type="family">Bowden</namePart>
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    <name type="personal">
        <namePart type="given">Marilyn</namePart>
        <namePart type="family">Walker</namePart>
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        <dateIssued>2019-oct–nov</dateIssued>
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            <title>Proceedings of the 12th International Conference on Natural Language Generation</title>
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            <namePart type="given">Kees</namePart>
            <namePart type="family">van Deemter</namePart>
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        <name type="personal">
            <namePart type="given">Chenghua</namePart>
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    <abstract>The uptake of deep learning in natural language generation (NLG) led to the release of both small and relatively large parallel corpora for training neural models. The existing data-to-text datasets are, however, aimed at task-oriented dialogue systems, and often thus limited in diversity and versatility. They are typically crowdsourced, with much of the noise left in them. Moreover, current neural NLG models do not take full advantage of large training data, and due to their strong generalizing properties produce sentences that look template-like regardless. We therefore present a new corpus of 7K samples, which (1) is clean despite being crowdsourced, (2) has utterances of 9 generalizable and conversational dialogue act types, making it more suitable for open-domain dialogue systems, and (3) explores the domain of video games, which is new to dialogue systems despite having excellent potential for supporting rich conversations.</abstract>
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    <identifier type="doi">10.18653/v1/W19-8623</identifier>
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        <url>https://aclanthology.org/W19-8623</url>
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        <date>2019-oct–nov</date>
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%0 Conference Proceedings
%T ViGGO: A Video Game Corpus for Data-To-Text Generation in Open-Domain Conversation
%A Juraska, Juraj
%A Bowden, Kevin
%A Walker, Marilyn
%Y van Deemter, Kees
%Y Lin, Chenghua
%Y Takamura, Hiroya
%S Proceedings of the 12th International Conference on Natural Language Generation
%D 2019
%8 oct–nov
%I Association for Computational Linguistics
%C Tokyo, Japan
%F juraska-etal-2019-viggo
%X The uptake of deep learning in natural language generation (NLG) led to the release of both small and relatively large parallel corpora for training neural models. The existing data-to-text datasets are, however, aimed at task-oriented dialogue systems, and often thus limited in diversity and versatility. They are typically crowdsourced, with much of the noise left in them. Moreover, current neural NLG models do not take full advantage of large training data, and due to their strong generalizing properties produce sentences that look template-like regardless. We therefore present a new corpus of 7K samples, which (1) is clean despite being crowdsourced, (2) has utterances of 9 generalizable and conversational dialogue act types, making it more suitable for open-domain dialogue systems, and (3) explores the domain of video games, which is new to dialogue systems despite having excellent potential for supporting rich conversations.
%R 10.18653/v1/W19-8623
%U https://aclanthology.org/W19-8623
%U https://doi.org/10.18653/v1/W19-8623
%P 164-172

Markdown (Informal)

[ViGGO: A Video Game Corpus for Data-To-Text Generation in Open-Domain Conversation](https://aclanthology.org/W19-8623) (Juraska et al., INLG 2019)

• ViGGO: A Video Game Corpus for Data-To-Text Generation in Open-Domain Conversation (Juraska et al., INLG 2019)

ACL

• Juraj Juraska, Kevin Bowden, and Marilyn Walker. 2019. ViGGO: A Video Game Corpus for Data-To-Text Generation in Open-Domain Conversation. In Proceedings of the 12th International Conference on Natural Language Generation, pages 164–172, Tokyo, Japan. Association for Computational Linguistics.