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Eye movement data during reading is a useful source of information forunderstanding language comprehension processes. In this paper, we describe oursubmission to the CMCL 2021 shared task on predicting human reading patterns.Our model uses RoBERTa with a regression layer to predict 5 eye-trackingfeatures. We train the model in two stages: we first fine-tune on the Provocorpus (another eye-tracking dataset), then fine-tune on the task data. Wecompare different Transformer models and apply ensembling methods to improvethe performance. Our final submission achieves a MAE score of 3.929, ranking3rd place out of 13 teams that participated in this shared task.

TorontoCL at CMCL 2021 Shared Task: RoBERTa with Multi-Stage Fine-Tuning for Eye-Tracking Prediction