Team Ohio State at CMCL 2021 Shared Task: Fine-Tuned RoBERTa for Eye-Tracking Data Prediction

NAACL (CMCL) 2021  ·  Byung-Doh Oh ·

This paper describes Team Ohio State’s approach to the CMCL 2021 Shared Task, the goal of which is to predict five eye-tracking features from naturalistic self-paced reading corpora. For this task, we fine-tune a pre-trained neural language model (RoBERTa; Liu et al., 2019) to predict each feature based on the contextualized representations. Moreover, motivated by previous eye-tracking studies, we include word length in characters and proportion of sentence processed as two additional input features. Our best model strongly outperforms the baseline and is also competitive with other systems submitted to the shared task. An ablation study shows that the word length feature contributes to making more accurate predictions, indicating the usefulness of features that are specific to the eye-tracking paradigm.

PDF Abstract


  Add Datasets introduced or used in this paper

Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.


No methods listed for this paper. Add relevant methods here