Bridging Information-Seeking Human Gaze and Machine Reading Comprehension
Name
2020.conll-1.11.pdf
Description
Published version
Size
482.3 KB
Format
Adobe PDF
Checksum (MD5)
794984dd145741f0f44965cf84279d00
Author(s)
Malmaud, Jonathan
Levy, Roger
Berzak, Yevgeni
Date Issued
2020
Journal
Proceedings of the 24th Conference on Computational Natural Language Learning
Publisher
Association for Computational Linguistics (ACL)
Citation
Malmaud, Jonathan, Levy, Roger and Berzak, Yevgeni. 2020. "Bridging Information-Seeking Human Gaze and Machine Reading Comprehension." Proceedings of the 24th Conference on Computational Natural Language Learning.
Version
Final published version
Abstract
In this work, we analyze how human gaze
during reading comprehension is conditioned
on the given reading comprehension question,
and whether this signal can be beneficial for
machine reading comprehension. To this end,
we collect a new eye-tracking dataset with a
large number of participants engaging in a multiple choice reading comprehension task. Our
analysis of this data reveals increased fixation
times over parts of the text that are most relevant for answering the question. Motivated
by this finding, we propose making automated
reading comprehension more human-like by
mimicking human information-seeking reading behavior during reading comprehension.
We demonstrate that this approach leads to performance gains on multiple choice question answering in English for a state-of-the-art reading comprehension model.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.18653/V1/2020.CONLL-1.11
