Few-Shot Dialogue Summarization via Skeleton-Assisted Prompt Transfer in Prompt Tuning

arXiv:2305.12077v2 Announce Type: replace
Abstract: In real-world scenarios, labeled samples for dialogue summarization are usually limited (i.e., few-shot) due to high annotation costs for high-quality dialogue summaries. To efficiently learn from few-shot samples, previous works have utilized massive annotated data from other downstream tasks and then performed prompt transfer in prompt tuning so as to enable cross-task knowledge transfer. However, existing general-purpose prompt transfer techniques lack consideration for dialogue-specific information. In this paper, we focus on improving the prompt transfer …

abstract annotated data annotation arxiv costs cs.cl data dialogue few-shot learn massive prompt prompt tuning quality samples summarization tasks transfer type via world