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.

翻译：具有语义相似细粒度意图的意图检测是一项具有挑战性的任务。为解决这一问题，我们将意图检测重新建模为问答检索任务，将用户语句和意图名称分别视为问题与答案。为此，我们采用问答检索架构，并引入基于批次对比损失的两阶段训练范式。在预训练阶段，通过自监督训练优化查询表示；随后在微调阶段，提升同一意图下问题与答案间基于上下文的词元级相似度得分。我们在三个少样本意图检测基准上取得了最优性能。