A Contrastive learning-based Task Adaptation model for few-shot intent recognition

作者：

Highlights：

• We employ a CTA model to get the task-unique feature for few-shot intent recognition.

• We introduce a contrastive-based loss function to well separate different classes.

• We use the semantics of label name as an anchor feature of each class to fix bias.

• Our model performs particularly well in multi-domain and cross-domain scenarios.

摘要

•We employ a CTA model to get the task-unique feature for few-shot intent recognition.•We introduce a contrastive-based loss function to well separate different classes.•We use the semantics of label name as an anchor feature of each class to fix bias.•Our model performs particularly well in multi-domain and cross-domain scenarios.

论文关键词：Intent recognition,Few-shot learning,Contrastive learning

论文评审过程：Received 6 October 2021, Revised 21 December 2021, Accepted 30 December 2021, Available online 7 February 2022, Version of Record 7 February 2022.

论文官网地址：https://doi.org/10.1016/j.ipm.2021.102863