Few-Shot Contrastive Learning-Based Multi-Round Dialogue Intent Classification Method

Abstract

Traditional text classification models face challenges in handling long texts and understanding topic transitions in dialogue scenarios, leading to suboptimal performance in automatic speech recognition (ASR)-based multi-round dialogue intent classification. In this article, we propose a few-shot contrastive learning-based multi-round dialogue intent classification method. First, the ASR texts are partitioned, and role-based features are extracted using a Transformer encoder. Second, refined sample pairs are forward-propagated, adversarial samples are generated by perturbing word embedding matrices and contrastive loss is applied to positive sample pairs. Then, positive sample pairs are input into a multi-round reasoning module to learn semantic clues from the entire scenario through multiple dialogues, obtain reasoning features, input them into a classifier to obtain classification results, and calculate multi-task loss. Finally, a prototype update module (PUM) is introduced to rectify the biased prototypes by using gated recurrent unit (GRU) to update the prototypes stored in the memory bank and few-shot learning (FSL) task. Experimental evaluations demonstrate that the proposed method outperforms state-of-the-art methods on two public datasets (DailyDialog and CM) and a private real-world dataset.