Multi-hop QA (Question Answering) is the task of finding the answer to a question across multiple documents. In recent years, a number of Deep Learning-based approaches have been proposed to tackle this complex task, as well as a few standard benchmarks to assess models Multi-hop QA capabilities. In this paper, we focus on the well-established HotpotQA benchmark dataset, which requires models to perform answer span extraction as well as support sentence prediction. We present two extensions to the SOTA Graph Neural Network (GNN) based model for HotpotQA, Hierarchical Graph Network (HGN): (i) we complete the original hierarchical structure by introducing new edges between the query and context sentence nodes; (ii) in the graph propagation step, we propose a novel extension to Hierarchical Graph Attention Network GATH (Graph ATtention with Hierarchies) that makes use of the graph hierarchy to update the node representations in a sequential fashion. Experiments on HotpotQA demonstrate the efficiency of the proposed modifications and support our assumptions about the effects of model related variables.

翻译：多跳问答（Multi-hop QA）是在多篇文档中寻找问题答案的任务。近年来，已提出大量基于深度学习的方法来处理这一复杂任务，并建立了若干标准基准以评估模型的多跳问答能力。本文聚焦于成熟的HotpotQA基准数据集，该数据集要求模型进行答案跨度提取和支持句子预测。我们针对HotpotQA的当前最优图神经网络模型——层次图网络（HGN），提出两项扩展： (i) 通过引入查询节点与上下文句子节点之间的新边，完善原始层次结构；(ii) 在图传播步骤中，提出层次图注意力网络GATH（Graph ATtention with Hierarchies）的新型扩展，利用图层次结构以序列方式更新节点表示。在HotpotQA上的实验证明了所提修改的有效性，并支持了我们对模型相关变量影响的假设。