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        <title>DIALKI: Knowledge Identification in Conversational Systems through Dialogue-Document Contextualization</title>
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    <name type="personal">
        <namePart type="given">Zeqiu</namePart>
        <namePart type="family">Wu</namePart>
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    <name type="personal">
        <namePart type="given">Bo-Ru</namePart>
        <namePart type="family">Lu</namePart>
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    <name type="personal">
        <namePart type="given">Hannaneh</namePart>
        <namePart type="family">Hajishirzi</namePart>
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    <name type="personal">
        <namePart type="given">Mari</namePart>
        <namePart type="family">Ostendorf</namePart>
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            <title>Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing</title>
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        <name type="personal">
            <namePart type="given">Marie-Francine</namePart>
            <namePart type="family">Moens</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Xuanjing</namePart>
            <namePart type="family">Huang</namePart>
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        <name type="personal">
            <namePart type="given">Lucia</namePart>
            <namePart type="family">Specia</namePart>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>Identifying relevant knowledge to be used in conversational systems that are grounded in long documents is critical to effective response generation. We introduce a knowledge identification model that leverages the document structure to provide dialogue-contextualized passage encodings and better locate knowledge relevant to the conversation. An auxiliary loss captures the history of dialogue-document connections. We demonstrate the effectiveness of our model on two document-grounded conversational datasets and provide analyses showing generalization to unseen documents and long dialogue contexts.</abstract>
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    <identifier type="doi">10.18653/v1/2021.emnlp-main.140</identifier>
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        <date>2021-11</date>
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%0 Conference Proceedings
%T DIALKI: Knowledge Identification in Conversational Systems through Dialogue-Document Contextualization
%A Wu, Zeqiu
%A Lu, Bo-Ru
%A Hajishirzi, Hannaneh
%A Ostendorf, Mari
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F wu-etal-2021-dialki
%X Identifying relevant knowledge to be used in conversational systems that are grounded in long documents is critical to effective response generation. We introduce a knowledge identification model that leverages the document structure to provide dialogue-contextualized passage encodings and better locate knowledge relevant to the conversation. An auxiliary loss captures the history of dialogue-document connections. We demonstrate the effectiveness of our model on two document-grounded conversational datasets and provide analyses showing generalization to unseen documents and long dialogue contexts.
%R 10.18653/v1/2021.emnlp-main.140
%U https://aclanthology.org/2021.emnlp-main.140
%U https://doi.org/10.18653/v1/2021.emnlp-main.140
%P 1852-1863

Markdown (Informal)

[DIALKI: Knowledge Identification in Conversational Systems through Dialogue-Document Contextualization](https://aclanthology.org/2021.emnlp-main.140) (Wu et al., EMNLP 2021)

• DIALKI: Knowledge Identification in Conversational Systems through Dialogue-Document Contextualization (Wu et al., EMNLP 2021)

ACL

• Zeqiu Wu, Bo-Ru Lu, Hannaneh Hajishirzi, and Mari Ostendorf. 2021. DIALKI: Knowledge Identification in Conversational Systems through Dialogue-Document Contextualization. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 1852–1863, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.