论文标题
通过基于路径的图形卷积网络跨文档的多跳读数理解
Multi-hop Reading Comprehension across Documents with Path-based Graph Convolutional Network
论文作者
论文摘要
跨多个文档的多跳阅读理解最近引起了很多关注。在本文中，我们提出了一种新的方法来解决这个多跳阅读理解问题。受到人类推理处理的启发，我们从支持文档中构建了一个基于路径的推理图。该图可以结合基于图和基于路径的方法的想法，因此更好地用于多跳推理。同时，我们提出封闭式RGCN在基于路径的推理图上积累证据，该图包含一种新的意识到的门控机制，以调节跨文档传播信息的实用性，并在推理过程中添加问题信息。我们在Wikihop数据集上评估了我们的方法，并且我们的方法可以针对先前发表的方法实现最新的准确性。特别是，我们的合奏模型超过了4.2％的人类绩效。
Multi-hop reading comprehension across multiple documents attracts much attention recently. In this paper, we propose a novel approach to tackle this multi-hop reading comprehension problem. Inspired by human reasoning processing, we construct a path-based reasoning graph from supporting documents. This graph can combine both the idea of the graph-based and path-based approaches, so it is better for multi-hop reasoning. Meanwhile, we propose Gated-RGCN to accumulate evidence on the path-based reasoning graph, which contains a new question-aware gating mechanism to regulate the usefulness of information propagating across documents and add question information during reasoning. We evaluate our approach on WikiHop dataset, and our approach achieves state-of-the-art accuracy against previously published approaches. Especially, our ensemble model surpasses human performance by 4.2%.
