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

TL;DR

This paper presents systems using pretrained language models like BERT and XLM to predict eye-tracking features, analyzing various factors affecting performance, and achieving competitive results in a shared task.

Contribution

It explores the use of multilingual pretrained models and different pooling strategies for eye-tracking prediction, including the impact of contextual and linguistic augmentation.

Findings

Achieved an average MAE of 5.72, ranking 5th in the shared task.

Post-evaluation, MAE improved to 5.25, indicating effective model tuning.

Different pooling and multilingual models influence prediction accuracy.

Abstract

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