QAID: Question Answering Inspired Few-shot Intent Detection

• URL: http://arxiv.org/abs/2303.01593v2
• Date: Tue, 21 Mar 2023 14:22:00 GMT
• Title: QAID: Question Answering Inspired Few-shot Intent Detection
• Authors: Asaf Yehudai, Matan Vetzler, Yosi Mass, Koren Lazar, Doron Cohen, Boaz Carmeli
• Abstract summary: We reformulate intent detection as a question-answering retrieval task by treating utterances and intent names as questions and answers. Our results on three few-shot intent detection benchmarks achieve state-of-the-art performance.
• Score: 5.516275800944541
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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