Multihop Question Answering is a complex Natural Language Processing task
that requires multiple steps of reasoning to find the correct answer to a given
question. Previous research has explored the use of models based on Graph
Neural Networks for tackling this task. Various architectures have been
proposed, including Relational Graph Convolutional Networks (RGCN). For these
many node types and relations between them have been introduced, such as simple
entity co-occurrences, modelling coreferences, or "reasoning paths" from
questions to answers via intermediary entities. Nevertheless, a thoughtful
analysis on which relations, node types, embeddings and architecture are the
most beneficial for this task is still missing. In this paper we explore a
number of RGCN-based Multihop QA models, graph relations, and node embeddings,
and empirically explore the influence of each on Multihop QA performance on the
WikiHop dataset.

本文旨在探索多站点问答任务的 RGCN 模型、图形关系和节点嵌入，并在 WikiHop 数据集上经验性地探讨每个关系、节点类型和嵌入对多站点 QA 性能的影响。

关系图卷积神经网络用于多跳推理的比较研究

Relational Graph Convolutional Neural Networks for Multihop Reasoning: A Comparative Study

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%.

本文提出了一种基于人类推理处理的新方法来解决多文档的多跳阅读理解问题，我们构建了一种基于路径的推理图来结合基于图和路径的方法，并使用了一个新的问题感知门控机制来调节在文档间传播的信息信息的有用性以及在推理期间添加问题信息。在 WikiHop 数据集上，我们的方法实现了先前发布方法的最新进展，尤其是我们的组合模型超过了人类的表现水平 4.2％。

基于路径图卷积神经网络的跨文档多跳阅读理解

Multi-hop Reading Comprehension across Documents with Path-based Graph  Convolutional Network

We propose a novel, path-based reasoning approach for the multi-hop reading
comprehension task where a system needs to combine facts from multiple passages
to answer a question. Although inspired by multi-hop reasoning over knowledge
graphs, our proposed approach operates directly over unstructured text. It
generates potential paths through passages and scores them without any direct
path supervision. The proposed model, named PathNet, attempts to extract
implicit relations from text through entity pair representations, and compose
them to encode each path. To capture additional context, PathNet also composes
the passage representations along each path to compute a passage-based
representation. Unlike previous approaches, our model is then able to explain
its reasoning via these explicit paths through the passages. We show that our
approach outperforms prior models on the multi-hop Wikihop dataset, and also
can be generalized to apply to the OpenBookQA dataset, matching
state-of-the-art performance.

本研究提出了一种基于路径的推理方法，用于多跳阅读理解任务，该方法结合了多个段落的事实来回答问题，并以 PathNet 模型为例，使用实体对表示方法从文本中提取隐含关系并编码路径。我们展示了该方法在多跳 Wikihop 数据集上的优异表现，并且可以推广到 OpenBookQA 数据集，达到最先进的性能水平。