@unpublished{yuan2019interactive,
author = {Yuan, Xingdi and Fu, Jie and Côté, Marc-Alexandre and Tay, Yi and Pal, Christopher and Trischler, Adam},
title = {Interactive Machine Comprehension with Information Seeking Agents},
year = {2019},
month = {August},
abstract = {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.},
url = {http://approjects.co.za/?big=en-us/research/publication/interactive-machine-comprehension-with-information-seeking-agents/},
}