Predicting Native Language from Gaze

Yevgeni Berzak, Chie Nakamura, Suzanne Flynn, Boris Katz


Abstract
A fundamental question in language learning concerns the role of a speaker’s first language in second language acquisition. We present a novel methodology for studying this question: analysis of eye-movement patterns in second language reading of free-form text. Using this methodology, we demonstrate for the first time that the native language of English learners can be predicted from their gaze fixations when reading English. We provide analysis of classifier uncertainty and learned features, which indicates that differences in English reading are likely to be rooted in linguistic divergences across native languages. The presented framework complements production studies and offers new ground for advancing research on multilingualism.
Anthology ID:
P17-1050
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
541–551
Language:
URL:
https://aclanthology.org/P17-1050
DOI:
10.18653/v1/P17-1050
Bibkey:
Cite (ACL):
Yevgeni Berzak, Chie Nakamura, Suzanne Flynn, and Boris Katz. 2017. Predicting Native Language from Gaze. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 541–551, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
Predicting Native Language from Gaze (Berzak et al., ACL 2017)
Copy Citation:
PDF:
https://aclanthology.org/P17-1050.pdf
Video:
 https://vimeo.com/234951959
Data
Penn Treebank