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Eye-Tracking data is a very useful source of information to study cognitionand especially language comprehension in humans. In this paper, we describe oursystems for the CMCL 2022 shared task on predicting eye-tracking information.We describe our experiments with pretrained models like BERT and XLM and thedifferent ways in which we used those representations to predict foureye-tracking features. Along with analysing the effect of using two differentkinds of pretrained multilingual language models and different ways of poolingthe tokenlevel representations, we also explore how contextual informationaffects the performance of the systems. Finally, we also explore if factorslike augmenting linguistic information affect the predictions. Our submissionsachieved an average MAE of 5.72 and ranked 5th in the shared task. The averageMAE showed further reduction to 5.25 in post task evaluation.

Team \'UFAL at CMCL 2022 Shared Task: Figuring out the correct recipe for predicting Eye-Tracking features using Pretrained Language Models