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arXiv · 1908.10449

Interactive Machine Comprehension with Information Seeking Agents

Xingdi Yuan·Jie Fu·Marc-Alexandre Cote·Yi Tay·Christopher Pal·Adam Trischler
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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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BibTeXRIS

Xingdi Yuan, Jie Fu, Marc-Alexandre Cote, Yi Tay, Christopher Pal, Adam Trischler. 2019-08-27. Interactive Machine Comprehension with Information Seeking Agents. https://arxiv.org/abs/1908.10449

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