A Method Based on Machine Reading Comprehension to Answer Multi-hop Complex Questions in Question Answering Systems

Question Answering Systems (QAS) are one of the most important intelligent systems that have the ability to provide instant and explicit answers to input questions. One of the new challenges of these systems is the ability to answer multi-hop complex questions that require information collection from multiple documents. In this paper, a method is proposed to solve the challenge of answering multi-hop complex questions. In this method, first, documents related to the question are retrieved in a two-step process. Then, to facilitate answering the question, knowledge is extracted from the retrieved documents and represented in the form of a graph. Finally, to find the answer to the question, reasoning is performed on the graph using a combination of heterogeneous graph neural network and transformer. To evaluate the effectiveness of the proposed method, this method and other related works have been tested on the HotpotQA open-domain dataset. The results obtained in answering questions based on F1 score and exact match are reported as 86.51 and 78.71 respectively, indicating superiority of this method over similar works.