@inproceedings{skerath-etal-2023-native,
title = "Native Language Prediction from Gaze: a Reproducibility Study",
author = "Skerath, Lina and
Toborek, Paulina and
Zieli{\'n}ska, Anita and
Barrett, Maria and
Van Der Goot, Rob",
editor = "Padmakumar, Vishakh and
Vallejo, Gisela and
Fu, Yao",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-srw.26",
doi = "10.18653/v1/2023.acl-srw.26",
pages = "152--159",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Native Language Prediction from Gaze: a Reproducibility Study
%A Skerath, Lina
%A Toborek, Paulina
%A Zielińska, Anita
%A Barrett, Maria
%A Van Der Goot, Rob
%Y Padmakumar, Vishakh
%Y Vallejo, Gisela
%Y Fu, Yao
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F skerath-etal-2023-native
%X 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.
%R 10.18653/v1/2023.acl-srw.26
%U https://aclanthology.org/2023.acl-srw.26
%U https://doi.org/10.18653/v1/2023.acl-srw.26
%P 152-159
Markdown (Informal)
[Native Language Prediction from Gaze: a Reproducibility Study](https://aclanthology.org/2023.acl-srw.26) (Skerath et al., ACL 2023)
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.