QAID: Question Answering Inspired Few-shot Intent Detection

TL;DR

This paper introduces QAID, a novel approach that reformulates intent detection as a question-answering task, leveraging a retrieval architecture and two-stage training to improve few-shot intent detection performance.

Contribution

The paper presents a new question-answering based framework for intent detection, with a two-stage training schema that enhances query representations and achieves state-of-the-art results.

Findings

Achieves state-of-the-art performance on three few-shot intent detection benchmarks.

Utilizes a two-stage training schema with self-supervised pre-training and fine-tuning.

Reformulates intent detection as a question-answering retrieval task.

Abstract

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.