IDIC-DST improves few-shot dialogue state tracking by extracting user intent to augment dialogue information and retrieve in-context examples, achieving reported SOTA on MultiWOZ 2.1 and 2.4.
Language models are few-shot learners[C]//Proceedings of the 34th International Conference on Neural Information Processing Systems
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Intent-driven In-context Learning for Few-shot Dialogue State Tracking
IDIC-DST improves few-shot dialogue state tracking by extracting user intent to augment dialogue information and retrieve in-context examples, achieving reported SOTA on MultiWOZ 2.1 and 2.4.