LAST at CMCL 2021 Shared Task: Predicting Gaze Data During Reading with a Gradient Boosting Decision Tree Approach

Yves Bestgen


Abstract
A LightGBM model fed with target word lexical characteristics and features obtained from word frequency lists, psychometric data and bigram association measures has been optimized for the 2021 CMCL Shared Task on Eye-Tracking Data Prediction. It obtained the best performance of all teams on two of the five eye-tracking measures to predict, allowing it to rank first on the official challenge criterion and to outperform all deep-learning based systems participating in the challenge.
Anthology ID:
2021.cmcl-1.10
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:
90–96
Language:
URL:
https://aclanthology.org/2021.cmcl-1.10
DOI:
10.18653/v1/2021.cmcl-1.10
Bibkey:
Cite (ACL):
Yves Bestgen. 2021. LAST at CMCL 2021 Shared Task: Predicting Gaze Data During Reading with a Gradient Boosting Decision Tree Approach. In Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics, pages 90–96, Online. Association for Computational Linguistics.
Cite (Informal):
LAST at CMCL 2021 Shared Task: Predicting Gaze Data During Reading with a Gradient Boosting Decision Tree Approach (Bestgen, CMCL 2021)
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PDF:
https://aclanthology.org/2021.cmcl-1.10.pdf