TY - JOUR T1 - Graph Reasoning and Context Fusion for Multi-Task, Multi-Hop Question Answering AU - Lee, Sangui AU - Kim, Incheol JO - KIPS Transactions on Software and Data Engineering PY - 2021 DA - 2021/1/30 DO - https://doi.org/10.3745/KTSDE.2021.10.8.319 KW - Open Domain Question Answering KW - Multi-hop Reasoning KW - Multi-task Question KW - Hierarchical Graph KW - Graph Neural Network AB - Recently, in the field of open domain natural language question answering, multi-task, multi-hop question answering has been studied extensively. In this paper, we propose a novel deep neural network model using hierarchical graphs to answer effectively such multi-task, multi-hop questions. The proposed model extracts different levels of contextual information from multiple paragraphs using hierarchical graphs and graph neural networks, and then utilize them to predict answer type, supporting sentences and answer spans simultaneously. Conducting experiments with the HotpotQA benchmark dataset, we show high performance and positive effects of the proposed model.