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.
@article{arxiv.2303.01593,
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},
journal= {arXiv preprint arXiv:2303.01593},
year = {2023}
}