{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/interactive-machine-comprehension-with","title":"Interactive Machine Comprehension with Information Seeking Agents","arxiv_id":"1908.10449","date":"2019-08-27","proceeding":"ACL 2020 6","authors":["Xingdi Yuan","Jie Fu","Marc-Alexandre Cote","Yi Tay","Christopher Pal","Adam Trischler"],"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. 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