Publikation: KonTra at CMCL 2021 Shared Task : Predicting Eye Movements by Combining BERT with Surface, Linguistic and Behavioral Information
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2021
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CHERSONI, Emmanuele, ed., Nora HOLLENSTEIN, ed., Cassandra JACOBS, ed. and others. Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics. Stroudsburg, PA: ACL, 2021, pp. 120-124. ISBN 978-1-954085-35-0. Available under: doi: 10.18653/v1/2021.cmcl-1.15
Zusammenfassung
This paper describes the submission of the team KonTra to the CMCL 2021 Shared Task on eye-tracking prediction. Our system combines the embeddings extracted from a fine-tuned BERT model with surface, linguistic and behavioral features, resulting in an average mean absolute error of 4.22 across all 5 eye-tracking measures. We show that word length and features representing the expectedness of a word are consistently the strongest predictors across all 5 eye-tracking measures.
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400 Sprachwissenschaft, Linguistik
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Workshop on Cognitive Modeling and Computational Linguistics (Online), 10. Juni 2021
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YU, Qi, Aikaterini-Lida KALOULI, Diego FRASSINELLI, 2021. KonTra at CMCL 2021 Shared Task : Predicting Eye Movements by Combining BERT with Surface, Linguistic and Behavioral Information. Workshop on Cognitive Modeling and Computational Linguistics (Online), 10. Juni 2021. In: CHERSONI, Emmanuele, ed., Nora HOLLENSTEIN, ed., Cassandra JACOBS, ed. and others. Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics. Stroudsburg, PA: ACL, 2021, pp. 120-124. ISBN 978-1-954085-35-0. Available under: doi: 10.18653/v1/2021.cmcl-1.15BibTex
@inproceedings{Yu2021KonTr-54528,
year={2021},
doi={10.18653/v1/2021.cmcl-1.15},
title={KonTra at CMCL 2021 Shared Task : Predicting Eye Movements by Combining BERT with Surface, Linguistic and Behavioral Information},
isbn={978-1-954085-35-0},
publisher={ACL},
address={Stroudsburg, PA},
booktitle={Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics},
pages={120--124},
editor={Chersoni, Emmanuele and Hollenstein, Nora and Jacobs, Cassandra},
author={Yu, Qi and Kalouli, Aikaterini-Lida and Frassinelli, Diego}
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<dcterms:abstract xml:lang="eng">This paper describes the submission of the team KonTra to the CMCL 2021 Shared Task on eye-tracking prediction. Our system combines the embeddings extracted from a fine-tuned BERT model with surface, linguistic and behavioral features, resulting in an average mean absolute error of 4.22 across all 5 eye-tracking measures. We show that word length and features representing the expectedness of a word are consistently the strongest predictors across all 5 eye-tracking measures.</dcterms:abstract>
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