Semantic Scholar Open Access 2021 48 sitasi

CMCL 2021 Shared Task on Eye-Tracking Prediction

Nora Hollenstein Emmanuele Chersoni Cassandra L. Jacobs Yohei Oseki Laurent Prévot +1 lainnya

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Abstrak

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.

Topik & Kata Kunci

Computer Science

Penulis (6)

Nora Hollenstein

Emmanuele Chersoni

Cassandra L. Jacobs

Yohei Oseki

Laurent Prévot

Enrico Santus

Format Sitasi

APA MLA BibTeX

Hollenstein, N., Chersoni, E., Jacobs, C.L., Oseki, Y., Prévot, L., Santus, E. (2021). CMCL 2021 Shared Task on Eye-Tracking Prediction. https://doi.org/10.18653/v1/2021.cmcl-1.7

Akses Cepat

Lihat di Sumber doi.org/10.18653/v1/2021.cmcl-1.7

Informasi Jurnal

Tahun Terbit: 2021
Bahasa: en
Total Sitasi: 48×
Sumber Database: Semantic Scholar
DOI: 10.18653/v1/2021.cmcl-1.7
Akses: Open Access ✓