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.- Anthology ID:
- 2021.cmcl-1.13
- 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:
- 108–113
- Language:
- URL:
- https://aclanthology.org/2021.cmcl-1.13
- DOI:
- 10.18653/v1/2021.cmcl-1.13
- Cite (ACL):
- Franck Dary, Alexis Nasr, and Abdellah Fourtassi. 2021. TALEP at CMCL 2021 Shared Task: Non Linear Combination of Low and High-Level Features for Predicting Eye-Tracking Data. In Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics, pages 108–113, Online. Association for Computational Linguistics.
- Cite (Informal):
- TALEP at CMCL 2021 Shared Task: Non Linear Combination of Low and High-Level Features for Predicting Eye-Tracking Data (Dary et al., CMCL 2021)
- PDF:
- https://preview.aclanthology.org/naacl-24-ws-corrections/2021.cmcl-1.13.pdf