Interactive Machine Comprehension with Information Seeking Agents
Xingdi Yuan, Jie Fu, Marc-Alexandre Côté, Yi Hong Tay, Chris Pal, Adam Trischler · 2020
Existing machine reading comprehension (MRC) models do not scale effectively to realworld 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.1