Design and Implementation of an Attention-Based Interpretable Model for Document Classification

Abstract

With the rapid development of artificial intelligence and machine learning, deep learning technology has been successfully applied to various tasks in the field of natural language processing. However, the erroneous decisions produced by the deep learning model and the characteristics of its black box cause users to doubt its decisions. We urgently need the model to have the ability to make rationalized explanations of its decisions, that is, interpretability. In this paper, we propose an attention-based interpretable model for document classification, which aims to have a better model performance and at the same time have better interpretability. Experiments show that our classification model is close to the performance of other mainstream models. Also, the attribution score explanation provided by the interpretation method are effective.