基于逻辑推理的机器阅读理解综述OA北大核心CSTPCD

Machine reading comprehension is one of the core tasks in the field of natural language processing,the goal of which is to enable machines to understand natural language text and correctly answer questions about the content of the text.With the development of methods and models related to natural language processing,researchers have begun to focus on the more challenging inferential problems in machine reading comprehension,which often require models to not only understand superficial information in text,but also to be able to think and reason logically to answer more complex questions.This paper gives a comprehensive summary of the latest achievements of machine reading comprehension based on logical reasoning.Firstly,the task of machine reading comprehension based on logical reasoning is introduced.Secondly,this paper introduces relevant methods for this task and classifies them into four categories based on different focuses:methods based on symbolic neural networks,methods based on graph neural networks,methods based on pre-training,and fine-tuning strategies based on large models,with a focus on describing the representative work of the four methods.Finally,the advantages and disadvantages of the methods are discussed on LogiQA and ReClor datasets,and the future research directions of machine reading comprehension task based on logical reasoning are summarized.