Multi-hop reading comprehension focuses on one type of factoid question,
where a system needs to properly integrate multiple pieces of evidence to
correctly answer a question. Previous work approximates global evidence with
local coreference information, encoding coreference chains with DAG-styled GRU
layers within a gated-attention reader. However, coreference is limited in
providing information for rich inference. We introduce a new method for better
connecting global evidence, which forms more complex graphs compared to DAGs.
To perform evidence integration on our graphs, we investigate two recent graph
neural networks, namely graph convolutional network (GCN) and graph recurrent
network (GRN). Experiments on two standard datasets show that richer global
information leads to better answers. Our method performs better than all
published results on these datasets.

通过新方法引入更多的全局证据来连接更复杂的图表，并使用图神经网络进行证据融合，从而提高了问题解答的效果。

探索基于图神经网络的图结构段落表示在多跳阅读理解中的应用

Exploring Graph-structured Passage Representation for Multi-hop Reading  Comprehension with Graph Neural Networks

We consider the problem of Recognizing Textual Entailment within an
Information Retrieval context, where we must simultaneously determine the
relevancy as well as degree of entailment for individual pieces of evidence to
determine a yes/no answer to a binary natural language question.
We compare several variants of neural networks for sentence embeddings in a
setting of decision-making based on evidence of varying relevance. We propose a
basic model to integrate evidence for entailment, show that joint training of
the sentence embeddings to model relevance and entailment is feasible even with
no explicit per-evidence supervision, and show the importance of evaluating
strong baselines. We also demonstrate the benefit of carrying over text
comprehension model trained on an unrelated task for our small datasets.
Our research is motivated primarily by a new open dataset we introduce,
consisting of binary questions and news-based evidence snippets. We also apply
the proposed relevance-entailment model on a similar task of ranking
multiple-choice test answers, evaluating it on a preliminary dataset of school
test questions as well as the standard MCTest dataset, where we improve the
neural model state-of-art.

研究了在信息检索中识别文本蕴含问题的模型，使用了多种变量的神经网络模型，提出了基本的蕴含证据集成模型并展示了其有效性，同时引入了一个基准数据集并在其上进行了实验，并应用该模型于多选题答案排序任务达到了最新的研究效果。