Zero Shot Crosslingual Eye-Tracking Data Prediction using Multilingual Transformer Models
This work addresses the challenge of crosslingual eye-tracking prediction for cognitive language research, but it is incremental as it builds on existing transformer models and shared task benchmarks.
The paper tackled the problem of predicting human reading patterns across multiple languages using eye-tracking data, achieving 4th place in SubTask-1 and 1st place in SubTask-2 in the CMCL 2022 shared task.
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.