Zero Shot Crosslingual Eye-Tracking Data Prediction using Multilingual Transformer Models

03/30/2022

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Eye tracking data during reading is a useful source of information to understand the cognitive processes that take place during language comprehension processes. Different languages account for different brain triggers , however there seems to be some uniform indicators. In this paper, we describe our submission to the CMCL 2022 shared task on predicting human reading patterns for multi-lingual dataset. Our model uses text representations from transformers and some hand engineered features with a regression layer on top to predict statistical measures of mean and standard deviation for 2 main eye-tracking features. We train an end to end model to extract meaningful information from different languages and test our model on two seperate datasets. We compare different transformer models and show ablation studies affecting model performance. Our final submission ranked 4th place for SubTask-1 and 1st place for SubTask-2 for the shared task.

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Zero Shot Crosslingual Eye-Tracking Data Prediction using Multilingual Transformer Models

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

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

Measuring the Impact of (Psycho-)Linguistic and Readability Features and Their Spill Over Effects on the Prediction of Eye Movement Patterns

WebQAmGaze: A Multilingual Webcam Eye-Tracking-While-Reading Dataset

LAST at CMCL 2021 Shared Task: Predicting Gaze Data During Reading with a Gradient Boosting Decision Tree Approach

Every word counts: A multilingual analysis of individual human alignment with model attention

Leveraging recent advances in Pre-Trained Language Models forEye-Tracking Prediction

Zero Shot Crosslingual Eye-Tracking Data Prediction using Multilingual Transformer Models

Related Research

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

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

Measuring the Impact of (Psycho-)Linguistic and Readability Features and Their Spill Over Effects on the Prediction of Eye Movement Patterns

WebQAmGaze: A Multilingual Webcam Eye-Tracking-While-Reading Dataset

LAST at CMCL 2021 Shared Task: Predicting Gaze Data During Reading with a Gradient Boosting Decision Tree Approach

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Leveraging recent advances in Pre-Trained Language Models forEye-Tracking Prediction