Native Language Prediction from Gaze: a Reproducibility Study

Lina Skerath, Paulina Toborek, Anita Zielińska, Maria Barrett, Rob Van Der Goot

Abstract
Numerous studies found that the linguistic properties of a person’s native language affect the cognitive processing of other languages. However, only one study has shown that it was possible to identify the native language based on eye-tracking records of natural L2 reading using machine learning. A new corpus allows us to replicate these results on a more interrelated and larger set of native languages. Our results show that comparable classification performance is maintained despite using less data. However, analysis shows that the correlation between L2 eye movements and native language similarity may be more complex than the original study found.
Anthology ID:
2023.acl-srw.26
Volume:
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Vishakh Padmakumar, Gisela Vallejo, Yao Fu
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
152–159
Language:
URL:
https://aclanthology.org/2023.acl-srw.26
DOI:
10.18653/v1/2023.acl-srw.26
Bibkey:
Cite (ACL):
Lina Skerath, Paulina Toborek, Anita Zielińska, Maria Barrett, and Rob Van Der Goot. 2023. Native Language Prediction from Gaze: a Reproducibility Study. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop), pages 152–159, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
Native Language Prediction from Gaze: a Reproducibility Study (Skerath et al., ACL 2023)
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