CMCL 2021 Shared Task on Eye-Tracking Prediction

Nora Hollenstein, Emmanuele Chersoni, Cassandra L. Jacobs, Yohei Oseki, Laurent Prévot, Enrico Santus

Abstract
Eye-tracking data from reading represent an important resource for both linguistics and natural language processing. The ability to accurately model gaze features is crucial to advance our understanding of language processing. This paper describes the Shared Task on Eye-Tracking Data Prediction, jointly organized with the eleventh edition of the Work- shop on Cognitive Modeling and Computational Linguistics (CMCL 2021). The goal of the task is to predict 5 different token- level eye-tracking metrics of the Zurich Cognitive Language Processing Corpus (ZuCo). Eye-tracking data were recorded during natural reading of English sentences. In total, we received submissions from 13 registered teams, whose systems include boosting algorithms with handcrafted features, neural models leveraging transformer language models, or hybrid approaches. The winning system used a range of linguistic and psychometric features in a gradient boosting framework.
Anthology ID:
2021.cmcl-1.7
Volume:
Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics
Month:
June
Year:
2021
Address:
Online
Editors:
Emmanuele Chersoni, Nora Hollenstein, Cassandra Jacobs, Yohei Oseki, Laurent Prévot, Enrico Santus
Venue:
CMCL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
72–78
Language:
URL:
https://aclanthology.org/2021.cmcl-1.7
DOI:
10.18653/v1/2021.cmcl-1.7
Bibkey:
Cite (ACL):
Nora Hollenstein, Emmanuele Chersoni, Cassandra L. Jacobs, Yohei Oseki, Laurent Prévot, and Enrico Santus. 2021. CMCL 2021 Shared Task on Eye-Tracking Prediction. In Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics, pages 72–78, Online. Association for Computational Linguistics.
Cite (Informal):
CMCL 2021 Shared Task on Eye-Tracking Prediction (Hollenstein et al., CMCL 2021)
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PDF:
https://preview.aclanthology.org/ingest-2024-clasp/2021.cmcl-1.7.pdf
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