Existing machine reading comprehension (MRC) models do not scale effectively
to real-world applications like web-level information retrieval and question
answering (QA). We argue that this stems from the nature of MRC datasets: most
of these are static environments wherein the supporting documents and all
necessary information are fully observed. In this paper, we propose a simple
method that reframes existing MRC datasets as interactive, partially observable
environments. Specifically, we "occlude" the majority of a document's text and
add context-sensitive commands that reveal "glimpses" of the hidden text to a
model. We repurpose SQuAD and NewsQA as an initial case study, and then show
how the interactive corpora can be used to train a model that seeks relevant
information through sequential decision making. We believe that this setting
can contribute in scaling models to web-level QA scenarios.

通过重新设计现有的机器阅读理解数据集，将其转化为互动、部分可观察的环境，加入上下文命令并训练模型，有望将模型扩展到面向 Web 级别的 QA 场景。