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        <title>Diverse Retrieval-Augmented In-Context Learning for Dialogue State Tracking</title>
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            <namePart type="given">Naoaki</namePart>
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    <abstract>There has been significant interest in zero and few-shot learning for dialogue state tracking (DST) due to the high cost of collecting and annotating task-oriented dialogues. Recent work has demonstrated that in-context learning requires very little data and zero parameter updates, and even outperforms trained methods in the few-shot setting. We propose RefPyDST, which advances the state of the art with three advancements to in-context learning for DST.First, we formulate DST as a Python programming task, explicitly modeling language coreference as variable reference in Python. Second, since in-context learning depends highly on the context examples, we propose a method to retrieve a diverse set of relevant examples to improve performance. Finally, we introduce a novel re-weighting method during decoding that takes into account probabilities of competing surface forms, and produces a more accurate dialogue state prediction. We evaluate our approach using MultiWOZ and achieve state-of-the-art multi-domain joint-goal accuracy in zero and few-shot settings.</abstract>
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%0 Conference Proceedings
%T Diverse Retrieval-Augmented In-Context Learning for Dialogue State Tracking
%A King, Brendan
%A Flanigan, Jeffrey
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F king-flanigan-2023-diverse
%X There has been significant interest in zero and few-shot learning for dialogue state tracking (DST) due to the high cost of collecting and annotating task-oriented dialogues. Recent work has demonstrated that in-context learning requires very little data and zero parameter updates, and even outperforms trained methods in the few-shot setting. We propose RefPyDST, which advances the state of the art with three advancements to in-context learning for DST.First, we formulate DST as a Python programming task, explicitly modeling language coreference as variable reference in Python. Second, since in-context learning depends highly on the context examples, we propose a method to retrieve a diverse set of relevant examples to improve performance. Finally, we introduce a novel re-weighting method during decoding that takes into account probabilities of competing surface forms, and produces a more accurate dialogue state prediction. We evaluate our approach using MultiWOZ and achieve state-of-the-art multi-domain joint-goal accuracy in zero and few-shot settings.
%R 10.18653/v1/2023.findings-acl.344
%U https://aclanthology.org/2023.findings-acl.344/
%U https://doi.org/10.18653/v1/2023.findings-acl.344
%P 5570-5585

Markdown (Informal)

[Diverse Retrieval-Augmented In-Context Learning for Dialogue State Tracking](https://aclanthology.org/2023.findings-acl.344/) (King & Flanigan, Findings 2023)

• Diverse Retrieval-Augmented In-Context Learning for Dialogue State Tracking (King & Flanigan, Findings 2023)

ACL

• Brendan King and Jeffrey Flanigan. 2023. Diverse Retrieval-Augmented In-Context Learning for Dialogue State Tracking. In Findings of the Association for Computational Linguistics: ACL 2023, pages 5570–5585, Toronto, Canada. Association for Computational Linguistics.