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.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CL 1years
2024 1verdicts
REJECT 1representative citing papers
citing papers explorer
-
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.