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QAID: Question Answering Inspired Few-shot Intent Detection

Asaf Yehudai · Matan Vetzler · Yosi Mass · Koren Lazar · Doron Cohen · Boaz Carmeli

MH1-2-3-4 #82

Keywords: [ Deep Learning and representational learning ] [ Passage Retrieval ] [ question answering ] [ contrastive learning ] [ Intent Detection ]


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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