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TALEP at CMCL 2021 Shared Task: Non Linear Combination of Low and High-Level Features for Predicting Eye-Tracking Data

Abstract : In this paper we describe our contribution to the CMCL 2021 Shared Task, which consists in predicting 5 different eye tracking variables from English tokenized text. Our approach is based on a neural network that combines both raw textual features we extracted from the text and parser-based features that include linguistic predictions (e.g. part of speech) and complexity metrics (e.g., entropy of parsing). We found that both the features we considered as well as the architecture of the neural model that combined these features played a role in the overall performance. Our system achieved relatively high accuracy on the test data of the challenge and was ranked 2nd out of 13 competing teams and a total of 30 submissions.
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https://hal.archives-ouvertes.fr/hal-03312501
Contributor : Franck Dary Connect in order to contact the contributor
Submitted on : Monday, August 2, 2021 - 3:14:47 PM
Last modification on : Monday, September 20, 2021 - 10:58:02 AM

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Franck Dary, Alexis Nasr, Abdellah Fourtassi. TALEP at CMCL 2021 Shared Task: Non Linear Combination of Low and High-Level Features for Predicting Eye-Tracking Data. Workshop on Cognitive Modeling and Computational Linguistics, Association for Computational Linguistics, Jun 2021, Online, Mexico. pp.108-113, ⟨10.18653/v1/2021.cmcl-1.13⟩. ⟨hal-03312501⟩

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