Multi-hop question answering using sparse graphs

Multi-hop question answering (QA) across multiple documents requires a deep understanding of relationships between entities in documents, questions, and answer candidates. Graph Neural Networks (GNNs) have emerged as a promising tool for multi-hop QA tasks. These models often suffer from increasing computational and model complexity, which makes them inefficient for real-world applications with limited resources. In this paper, we propose a graph-based approach called Sparse Graph-based Multi-hop Question Answering system (SG-MQA), which provides a throughout examination of the mentioned challenges and presents appropriate measures to address them. We propose a novel approach based on the Relational Graph Convolutional Network (R-GCN) that reduces the model complexity and improves its performance. We have utilized various strategies and conducted multiple experiments to achieve this goal. We show the efficacy of the proposed approach by examining the results of experiments on two QA datasets, namely WikiHop and HotpotQA. The SG-MQA model outperforms all the state-of-the-art (SOTA) methods on WikiHop and increases the accuracy of the best previous approach from 74.4% to 78.3%. Additionally, it achieves acceptable performance on HotpotQA. Although, according to the F1 measure, the performance of SG-MQA is inferior to that of the SOTA model, it is comparable to that of all other approaches. On the other hand, based on the Exact Match (EM) measure, SG-MQA shows comparable performance to that of the SOTA model and outperforms all other approaches.