@inproceedings{choudhary-etal-2021-mtl782,
    title = "{MTL}782{\_}{IITD} at {CMCL} 2021 Shared Task: Prediction of Eye-Tracking Features Using {BERT} Embeddings and Linguistic Features",
    author = "Choudhary, Shivani  and
      Tandon, Kushagri  and
      Agarwal, Raksha  and
      Chatterjee, Niladri",
    editor = "Chersoni, Emmanuele  and
      Hollenstein, Nora  and
      Jacobs, Cassandra  and
      Oseki, Yohei  and
      Pr{\'e}vot, Laurent  and
      Santus, Enrico",
    booktitle = "Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2021.cmcl-1.14/",
    doi = "10.18653/v1/2021.cmcl-1.14",
    pages = "114--119",
    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."
}