![]() Our results suggest that utilizing language- and task-specific adapters is beneficial and translating test sets into similar languages that exist in the training set could help with zero-shot transferability in the prediction of human reading behavior. We find that multilingual models equipped with adapters perform well in predicting eye-tracking features. To this end, we train ‘adapters’ inserted into the layers of frozen transformer-based pretrained language models. The shared task is focused on multi- and cross-lingual prediction of eye movement features in human reading behavior, which could provide valuable information regarding language processing. Abstract In this paper, we present the details of our approaches that attained the second place in the shared task of the ACL 2022 Cognitive Modeling and Computational Linguistics Workshop. ![]()
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March 2023
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