Diverse Retrieval-Augmented In-Context Learning for Dialogue State Tracking

• URL: http://arxiv.org/abs/2307.01453v1
• Date: Tue, 4 Jul 2023 03:15:52 GMT
• Title: Diverse Retrieval-Augmented In-Context Learning for Dialogue State Tracking
• Authors: Brendan King and Jeffrey Flanigan
• Abstract summary: We propose RefPyDST, which advances the state of the art with three advancements to in-context learning for dialogue state tracking. 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.
• Score: 3.8073142980733
• License: http://creativecommons.org/licenses/by/4.0/
• 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 (Hu et al. 2022). 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.

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