QAID: Question Answering Inspired Few-shot 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 finetuning 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. Intent detection (ID) is the task of classifying an incoming user query to one class from a set of mutually-exclusive classes, a.k.a. This ability is a cornerstone for task-oriented dialogue systems as correctly identifying the user intent at the beginning of an interaction is crucial to its success. However, labeled data is required for training and manual annotation is costly. This calls for sample efficient methods, gaining high accuracy with minimal amounts of labeled data.