QAID: Question Answering Inspired Few-shot Intent Detection
Asaf Yehudai, Matan Vetzler, Yosi Mass, Koren Lazar, Doron Cohen, Boaz Carmeli
Abstract
Intent detection with semantically similar fine-grained intents is a challenging task. To address it, we reformulate intent detection as a question-answering retrieval task by treating utterances and intent names as questions and answers. To that end, we utilize a question-answering retrieval architecture and adopt a two stages training schema with batch contrastive loss. In the pre-training stage, we improve query representations through self-supervised training. Then, in the fine-tuning stage, we increase contextualized token-level similarity scores between queries and answers from the same intent. Our results on three few-shot intent detection benchmarks achieve state-of-the-art performance.
BibTeX
@inproceedings{
yehudai2023qaid,
title={{QAID}: Question Answering Inspired Few-shot Intent Detection},
author={Asaf Yehudai and Matan Vetzler and Yosi Mass and Koren Lazar and Doron Cohen and Boaz Carmeli},
booktitle={The Eleventh International Conference on Learning Representations },
year={2023},
url={https://openreview.net/forum?id=gNI4_85Cyve}
}