Research Progress of Multi-Hop Machine Reading Comprehension

Abstract

Compared with common single-hop Machine Reading Comprehension(MRC), Multi-Hop MRC(MHMRC) needs multi-hop reasoning from given multiple documents or paragraphs to understand and answer complex questions.Though MHMRC is extremely challenging, it is closer to human language and reasoning, and has broad application prospects.The research background of MHMRC is introduced and the existing methods are divided according to the applicable scenarios into closed set Question Answering(QA) and Open-domain Question Answering(OpenQA), mainly including methods based on question decomposition, methods based on Graph Neural Network(GNN), methods improving index and methods based on reasoning path, etc.The methods are comprehensively analyzed from the perspectives of model architecture, features, advantages and disadvantages.Then unstructured text datasets and indexes for MHMRC evaluation are described, and they are employed to compare the performance of each model.On this basis, the paper discusses the challenges and hotspots of MHMRC research and the trends of future development are discussed.