@article{M6FAB2A46,
title = "Graph Reasoning and Context Fusion for Multi-Task,
Multi-Hop Question Answering",
journal = "The Transactions of the Korea Information Processing Society",
year = "2021",
issn = "null",
doi = "https://doi.org/10.3745/KTSDE.2021.10.8.319",
author = "Sangui Lee/Incheol Kim",
keywords = "Open Domain Question Answering, Multi-hop Reasoning, Multi-task Question, Hierarchical Graph, Graph Neural Network",
abstract = "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."
}