Natural Questions is a new challenging machine reading comprehension
benchmark with two-grained answers, which are a long answer (typically a
paragraph) and a short answer (one or more entities inside the long answer).
Despite the effectiveness of existing methods on this benchmark, they treat
these two sub-tasks individually during training while ignoring their
dependencies. To address this issue, we present a novel multi-grained machine
reading comprehension framework that focuses on modeling documents at their
hierarchical nature, which are different levels of granularity: documents,
paragraphs, sentences, and tokens. We utilize graph attention networks to
obtain different levels of representations so that they can be learned
simultaneously. The long and short answers can be extracted from
paragraph-level representation and token-level representation, respectively. In
this way, we can model the dependencies between the two-grained answers to
provide evidence for each other. We jointly train the two sub-tasks, and our
experiments show that our approach significantly outperforms previous systems
at both long and short answer criteria.

本文提出了一种新的多粒度机器阅读理解框架，利用图注意力网络对文档的不同层次进行建模，以同时学习各个级别的表示，从而提取长答案和短答案并建模其之间的依赖关系，并通过联合训练来显著提高对两种类型答案的准确性。