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        <title>Semantic Parsing by Large Language Models for Intricate Updating Strategies of Zero-Shot Dialogue State Tracking</title>
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    <name type="personal">
        <namePart type="given">Yuxiang</namePart>
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        <namePart type="given">Guanting</namePart>
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    <name type="personal">
        <namePart type="given">Weiran</namePart>
        <namePart type="family">Xu</namePart>
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    <abstract>Zero-shot Dialogue State Tracking (DST) addresses the challenge of acquiring and annotating task-oriented dialogues, which can be time-consuming and costly. However, DST extends beyond simple slot-filling and requires effective updating strategies for tracking dialogue state as conversations progress. In this paper, we propose ParsingDST, a new In-Context Learning (ICL) method, to introduce additional intricate updating strategies in zero-shot DST. Our approach reformulates the DST task by leveraging powerful Large Language Models (LLMs) and translating the original dialogue text to JSON through semantic parsing as an intermediate state. We also design a novel framework that includes more modules to ensure the effectiveness of updating strategies in the text-to-JSON process. Experimental results demonstrate that our approach outperforms existing zero-shot DST methods on MultiWOZ, exhibiting significant improvements in Joint Goal Accuracy (JGA) and slot accuracy compared to existing ICL methods.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-emnlp.741</identifier>
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%0 Conference Proceedings
%T Semantic Parsing by Large Language Models for Intricate Updating Strategies of Zero-Shot Dialogue State Tracking
%A Wu, Yuxiang
%A Dong, Guanting
%A Xu, Weiran
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F wu-etal-2023-semantic
%X Zero-shot Dialogue State Tracking (DST) addresses the challenge of acquiring and annotating task-oriented dialogues, which can be time-consuming and costly. However, DST extends beyond simple slot-filling and requires effective updating strategies for tracking dialogue state as conversations progress. In this paper, we propose ParsingDST, a new In-Context Learning (ICL) method, to introduce additional intricate updating strategies in zero-shot DST. Our approach reformulates the DST task by leveraging powerful Large Language Models (LLMs) and translating the original dialogue text to JSON through semantic parsing as an intermediate state. We also design a novel framework that includes more modules to ensure the effectiveness of updating strategies in the text-to-JSON process. Experimental results demonstrate that our approach outperforms existing zero-shot DST methods on MultiWOZ, exhibiting significant improvements in Joint Goal Accuracy (JGA) and slot accuracy compared to existing ICL methods.
%R 10.18653/v1/2023.findings-emnlp.741
%U https://aclanthology.org/2023.findings-emnlp.741
%U https://doi.org/10.18653/v1/2023.findings-emnlp.741
%P 11093-11099

Markdown (Informal)

[Semantic Parsing by Large Language Models for Intricate Updating Strategies of Zero-Shot Dialogue State Tracking](https://aclanthology.org/2023.findings-emnlp.741) (Wu et al., Findings 2023)

• Semantic Parsing by Large Language Models for Intricate Updating Strategies of Zero-Shot Dialogue State Tracking (Wu et al., Findings 2023)

ACL

• Yuxiang Wu, Guanting Dong, and Weiran Xu. 2023. Semantic Parsing by Large Language Models for Intricate Updating Strategies of Zero-Shot Dialogue State Tracking. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 11093–11099, Singapore. Association for Computational Linguistics.