A Classification Model of Legal Consulting Questions Based on Multi-Attention Prototypical Networks

Text classification is a research hotspot in the field of natural language processing. Existing text classification models based on supervised learning, especially deep learning models, have made great progress on public datasets. But most of these methods rely on a large amount of training data, and these datasets coverage is limited. In the legal intelligent question-answering system, accurate classification of legal consulting questions is a necessary prerequisite for the realization of intelligent question answering. However, due to lack of sufficient annotation data and the cost of labeling is high, which lead to the poor effect of traditional supervised learning methods under sparse labeling. In response to the above problems, we construct a few-shot legal consulting questions dataset, and propose a prototypical networks model based on multi-attention. For the same category of instances, this model first highlights the key features in the instances as much as possible through instance-dimension level attention. Then it realizes the classification of legal consulting questions by prototypical networks. Experimental results show that our model achieves state-of-the-art results compared with baseline models. The code and dataset are released on https://github.com/cjm0824/MAPN.