QAID: Question Answering Inspired Few-shot Intent Detection
ICLR 2023(2023)
摘要
Intent detection with semantically similar fine-grained intents is a challenging task. To address it, we reformulate intent detection as a question-answering 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 first stage, we train the model to learn better query representation in a self supervise manner. Then, in the second stage, we fine-tune the model to optimize 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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关键词
Intent Detection,Question Answering,Contrastive Learning,Passage Retrieval
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