Interactive Machine Comprehension with Information Seeking Agents

Xingdi Yuan, Jie Fu, Marc-Alexandre Côté, Yi Tay, Chris 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. We believe that this setting can contribute in scaling models to web-level QA scenarios.
Anthology ID:
2020.acl-main.211
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2325–2338
Language:
URL:
https://aclanthology.org/2020.acl-main.211
DOI:
10.18653/v1/2020.acl-main.211
Bibkey:
Cite (ACL):
Xingdi Yuan, Jie Fu, Marc-Alexandre Côté, Yi Tay, Chris Pal, and Adam Trischler. 2020. Interactive Machine Comprehension with Information Seeking Agents. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 2325–2338, Online. Association for Computational Linguistics.
Cite (Informal):
Interactive Machine Comprehension with Information Seeking Agents (Yuan et al., ACL 2020)
Copy Citation:
PDF:
https://preview.aclanthology.org/nodalida-main-page/2020.acl-main.211.pdf
Video:
 http://slideslive.com/38929112
Code
 xingdi-eric-yuan/imrc_public
Data
NewsQA