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Few-Shot Contrastive Learning-Based Multi-Round Dialogue Intent Classification Method
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DOI：10.1111/exsy.13771.png)
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Abstract
En 中文
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.
Keywords:
AI in convergence ICT
few-shot contrastive learning
intent classification
multi-round dialogue
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