  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.
