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        <title>Dialogue State Tracking with a Language Model using Schema-Driven Prompting</title>
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    <name type="personal">
        <namePart type="given">Chia-Hsuan</namePart>
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    <name type="personal">
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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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        <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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    <abstract>Task-oriented conversational systems often use dialogue state tracking to represent the user’s intentions, which involves filling in values of pre-defined slots. Many approaches have been proposed, often using task-specific architectures with special-purpose classifiers. Recently, good results have been obtained using more general architectures based on pretrained language models. Here, we introduce a new variation of the language modeling approach that uses schema-driven prompting to provide task-aware history encoding that is used for both categorical and non-categorical slots. We further improve performance by augmenting the prompting with schema descriptions, a naturally occurring source of in-domain knowledge. Our purely generative system achieves state-of-the-art performance on MultiWOZ 2.2 and achieves competitive performance on two other benchmarks: MultiWOZ 2.1 and M2M. The data and code will be available at https://github.com/chiahsuan156/DST-as-Prompting.</abstract>
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    <identifier type="doi">10.18653/v1/2021.emnlp-main.404</identifier>
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%0 Conference Proceedings
%T Dialogue State Tracking with a Language Model using Schema-Driven Prompting
%A Lee, Chia-Hsuan
%A Cheng, Hao
%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 lee-etal-2021-dialogue
%X Task-oriented conversational systems often use dialogue state tracking to represent the user’s intentions, which involves filling in values of pre-defined slots. Many approaches have been proposed, often using task-specific architectures with special-purpose classifiers. Recently, good results have been obtained using more general architectures based on pretrained language models. Here, we introduce a new variation of the language modeling approach that uses schema-driven prompting to provide task-aware history encoding that is used for both categorical and non-categorical slots. We further improve performance by augmenting the prompting with schema descriptions, a naturally occurring source of in-domain knowledge. Our purely generative system achieves state-of-the-art performance on MultiWOZ 2.2 and achieves competitive performance on two other benchmarks: MultiWOZ 2.1 and M2M. The data and code will be available at https://github.com/chiahsuan156/DST-as-Prompting.
%R 10.18653/v1/2021.emnlp-main.404
%U https://aclanthology.org/2021.emnlp-main.404
%U https://doi.org/10.18653/v1/2021.emnlp-main.404
%P 4937-4949

Markdown (Informal)

[Dialogue State Tracking with a Language Model using Schema-Driven Prompting](https://aclanthology.org/2021.emnlp-main.404) (Lee et al., EMNLP 2021)

• Dialogue State Tracking with a Language Model using Schema-Driven Prompting (Lee et al., EMNLP 2021)

ACL

• Chia-Hsuan Lee, Hao Cheng, and Mari Ostendorf. 2021. Dialogue State Tracking with a Language Model using Schema-Driven Prompting. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 4937–4949, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.