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        <title>A Dataset for Document Grounded Conversations</title>
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        <namePart type="given">Kangyan</namePart>
        <namePart type="family">Zhou</namePart>
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        <namePart type="given">Shrimai</namePart>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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    <abstract>This paper introduces a document grounded dataset for conversations. We define “Document Grounded Conversations” as conversations that are about the contents of a specified document. In this dataset the specified documents were Wikipedia articles about popular movies. The dataset contains 4112 conversations with an average of 21.43 turns per conversation. This positions this dataset to not only provide a relevant chat history while generating responses but also provide a source of information that the models could use. We describe two neural architectures that provide benchmark performance on the task of generating the next response. We also evaluate our models for engagement and fluency, and find that the information from the document helps in generating more engaging and fluent responses.</abstract>
    <identifier type="citekey">zhou-etal-2018-dataset</identifier>
    <identifier type="doi">10.18653/v1/D18-1076</identifier>
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        <date>2018-oct-nov</date>
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%0 Conference Proceedings
%T A Dataset for Document Grounded Conversations
%A Zhou, Kangyan
%A Prabhumoye, Shrimai
%A Black, Alan W.
%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 zhou-etal-2018-dataset
%X This paper introduces a document grounded dataset for conversations. We define “Document Grounded Conversations” as conversations that are about the contents of a specified document. In this dataset the specified documents were Wikipedia articles about popular movies. The dataset contains 4112 conversations with an average of 21.43 turns per conversation. This positions this dataset to not only provide a relevant chat history while generating responses but also provide a source of information that the models could use. We describe two neural architectures that provide benchmark performance on the task of generating the next response. We also evaluate our models for engagement and fluency, and find that the information from the document helps in generating more engaging and fluent responses.
%R 10.18653/v1/D18-1076
%U https://aclanthology.org/D18-1076
%U https://doi.org/10.18653/v1/D18-1076
%P 708-713

Markdown (Informal)

[A Dataset for Document Grounded Conversations](https://aclanthology.org/D18-1076) (Zhou et al., EMNLP 2018)

• A Dataset for Document Grounded Conversations (Zhou et al., EMNLP 2018)

ACL

• Kangyan Zhou, Shrimai Prabhumoye, and Alan W Black. 2018. A Dataset for Document Grounded Conversations. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 708–713, Brussels, Belgium. Association for Computational Linguistics.