Interactive Machine Comprehension with Information Seeking Agents

Xingdi Yuan, Jie Fu, Marc-Alexandre Côté, Yi Tay, Chris Pal, Adam Trischler

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Semantics: Textual Inference and Other Areas of Semantics Long Paper

Session 4A: Jul 6 (17:00-18:00 GMT)
Session 5A: Jul 6 (20:00-21:00 GMT)
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.
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