Comparing and Predicting Eye-tracking Data of Mandarin and Cantonese

Junlin Li, Bo Peng, Yu-yin Hsu, Emmanuele Chersoni


Abstract
Eye-tracking data in Chinese languages present unique challenges due to the non-alphabetic and unspaced nature of the Chinese writing systems. This paper introduces the first deeply-annotated joint Mandarin-Cantonese eye-tracking dataset, from which we achieve a unified eye-tracking prediction system for both language varieties. In addition to the commonly studied first fixation duration and the total fixation duration, this dataset also includes the second fixation duration, expressing fixation patterns that are more relevant to higher-level, structural processing. A basic comparison of the features and measurements in our dataset revealed variation between Mandarin and Cantonese on fixation patterns related to word class and word position. The test of feature usefulness suggested that traditional features are less powerful in predicting the second-pass fixation, to which the linear distance to root makes a leading contribution in Mandarin. In contrast, Cantonese eye-movement behavior relies more on word position and part of speech.
Anthology ID:
2023.vardial-1.12
Volume:
Tenth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2023)
Month:
May
Year:
2023
Address:
Dubrovnik, Croatia
Editors:
Yves Scherrer, Tommi Jauhiainen, Nikola Ljubešić, Preslav Nakov, Jörg Tiedemann, Marcos Zampieri
Venue:
VarDial
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
121–132
Language:
URL:
https://aclanthology.org/2023.vardial-1.12
DOI:
10.18653/v1/2023.vardial-1.12
Bibkey:
Cite (ACL):
Junlin Li, Bo Peng, Yu-yin Hsu, and Emmanuele Chersoni. 2023. Comparing and Predicting Eye-tracking Data of Mandarin and Cantonese. In Tenth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2023), pages 121–132, Dubrovnik, Croatia. Association for Computational Linguistics.
Cite (Informal):
Comparing and Predicting Eye-tracking Data of Mandarin and Cantonese (Li et al., VarDial 2023)
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PDF:
https://aclanthology.org/2023.vardial-1.12.pdf
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 https://aclanthology.org/2023.vardial-1.12.mp4