Cross-Lingual Transfer Learning for Enhanced Synthetic Scanpath Prediction in Reading

Eye movements during reading provide valuable data for studying cognitive processes and text properties. While eye-tracking data can enhance language models, its application is limited by data scarcity, particularly for low-resource languages. Although synthetic scanpath generation offers a promising solution, current models are largely confined to English and Chinese. This work explores multilingual model for scanpath generation, demonstrating that a single model trained on 13 typologically diverse languages outperforms monolingual counterparts across most metrics. By leveraging shared linguistic patterns, our approach mitigates data scarcity for individual languages and facilitates synthetic gaze data creation for broader multilingual NLP applications.