Abstract: Multi-hop machine reading comprehension is one of the most difficult tasks in the field of natural language processing, which requires reasoning between multiple paragraphs. The complex question in multi-hop machine reading comprehension task is usually composed of several simple questions and decomposing the complex question can make the model better understand the question itself. Therefore, for the complex multi-hop question, a multi-hop reading comprehension model based on question decomposition is proposed. The multi-hop question is first decomposed into several single-hop questions, and then the single-hop reading comprehension model is used to solve them. The question decomposition is regarded as a reading comprehension task: the multi-hop question is the context of the question decomposition, while the evidence paragraph containing the answer to the question is the question. Machine reading comprehension task captures the interactive semantic information between multi-hop question and evidence paragraph, which can guide the extraction of single-hop questions in multi-hop questions. The values of the proposed model on BLEU and Rouge-L are 71.48% and 79.29%, respectively. Experimental results show that this model is effective for multi-hop machine reading comprehension.

Key words: machine reading comprehension, reasoning, multi-hop question, question decomposition ,