@inproceedings{699548871ac8425dbbec74b1a5b382af,
title = "Bridging Information-Seeking Human Gaze and Machine Reading Comprehension",
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.",
author = "Jonathan Malmaud and Roger Levy and Yevgeni Berzak",
note = "Publisher Copyright: {\textcopyright} 2020 Association for Computational Linguistics.; 24th Conference on Computational Natural Language Learning, CoNLL 2020 ; Conference date: 19-11-2020 Through 20-11-2020",
year = "2020",
doi = "https://doi.org/10.18653/v1/2020.conll-1.11",
language = "الإنجليزيّة",
series = "CoNLL 2020 - 24th Conference on Computational Natural Language Learning, Proceedings of the Conference",
pages = "142--152",
editor = "Raquel Fernandez and Tal Linzen",
booktitle = "CoNLL 2020 - 24th Conference on Computational Natural Language Learning, Proceedings of the Conference",
}
