A combinatorial scoring method for retriever training data improves few-shot dialogue state tracking by 20x in data efficiency and by 12% in oracle upper-bound JGA over prior methods.
MultiWOZ 2.3: A multi-domain task-oriented dialogue dataset enhanced with annotation corrections and co-reference annotation
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abstract
Task-oriented dialogue systems have made unprecedented progress with multiple state-of-the-art (SOTA) models underpinned by a number of publicly available MultiWOZ datasets. Dialogue state annotations are error-prone, leading to sub-optimal performance. Various efforts have been put in rectifying the annotation errors presented in the original MultiWOZ dataset. In this paper, we introduce MultiWOZ 2.3, in which we differentiate incorrect annotations in dialogue acts from dialogue states, identifying a lack of co-reference when publishing the updated dataset. To ensure consistency between dialogue acts and dialogue states, we implement co-reference features and unify annotations of dialogue acts and dialogue states. We update the state of the art performance of natural language understanding and dialogue state tracking on MultiWOZ 2.3, where the results show significant improvements than on previous versions of MultiWOZ datasets (2.0-2.2).
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cs.CL 1years
2025 1verdicts
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Improving Dialogue State Tracking through Combinatorial Search for In-Context Examples
A combinatorial scoring method for retriever training data improves few-shot dialogue state tracking by 20x in data efficiency and by 12% in oracle upper-bound JGA over prior methods.