MTL782_IITD at CMCL 2021 Shared Task: Prediction of Eye-Tracking Features Using BERT Embeddings and Linguistic Features

Shivani Choudhary, Kushagri Tandon, Raksha Agarwal, Niladri Chatterjee


Abstract
Reading and comprehension are quintessentially cognitive tasks. Eye movement acts as a surrogate to understand which part of a sentence is critical to the process of comprehension. The aim of the shared task is to predict five eye-tracking features for a given word of the input sentence. We experimented with several models based on LGBM (Light Gradient Boosting Machine) Regression, ANN (Artificial Neural Network), and CNN (Convolutional Neural Network), using BERT embeddings and some combination of linguistic features. Our submission using CNN achieved an average MAE of 4.0639 and ranked 7th in the shared task. The average MAE was further lowered to 3.994 in post-task evaluation.
Anthology ID:
2021.cmcl-1.14
Volume:
Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics
Month:
June
Year:
2021
Address:
Online
Venues:
CMCL | NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
114–119
Language:
URL:
https://aclanthology.org/2021.cmcl-1.14
DOI:
10.18653/v1/2021.cmcl-1.14
Bibkey:
Cite (ACL):
Shivani Choudhary, Kushagri Tandon, Raksha Agarwal, and Niladri Chatterjee. 2021. MTL782_IITD at CMCL 2021 Shared Task: Prediction of Eye-Tracking Features Using BERT Embeddings and Linguistic Features. In Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics, pages 114–119, Online. Association for Computational Linguistics.
Cite (Informal):
MTL782_IITD at CMCL 2021 Shared Task: Prediction of Eye-Tracking Features Using BERT Embeddings and Linguistic Features (Choudhary et al., CMCL 2021)
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PDF:
https://aclanthology.org/2021.cmcl-1.14.pdf
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 2021.cmcl-1.14.OptionalSupplementaryData.zip