Few-shot intent detection with self-supervised pretraining and prototype-aware attention

Few -shot intent detection is a more challenging application. However, traditional prototypical networks based on averaging often suffer from issues such as missing key information, poor generalization capabilities. In previous work, using three-dimensional convolutional neural networks (3DCNN) to generate prototype representations faces challenges with long-distance dependencies. Furthermore, a pretrained encoder's performance in a specific domain is often suboptimal because its knowledge of the specific domain is fragmented. Therefore, in this paper, we propose a simple yet effective two -stage learning strategy to address these issues. In the first stage, we propose a self -supervised multi -task pretraining (SMTP) strategy. SMTP utilizes unlabeled data from the current domain to help the pretrained encoder learn the semantic information of the text and implicitly distinguish semantically similar text representations without using any labels. SMTP aims to enhance the representation capability of the pretrained encoder in a specific domain. In the second stage, we propose a prototype -aware attention (PaAT) model to generate prototype representations of the same class. PaAT generates prototype representations by calculating the attention between class texts, which can effectively solve the long-distance dependence problem of 3DCNN. PaAT is a siamese architecture that can simultaneously generate prototype representations and sentence -level representations of unseen data. In addition, to prevent overfitting in few -shot learning, we introduce an unsupervised contrastive regularization term to constrain PaAT. Our method achieves state-of-the-art performance on four public datasets. 1