Do Transformer Models Show Similar Attention Patterns to Task-Specific Human Gaze?
Oliver Eberle, Stephanie Brandl, Jonas Pilot, Anders Søgaard · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2022
Learned self-attention functions in state-of-theart NLP models often correlate with human attention.We investigate whether self-attention in large-scale pre-trained language models is as predictive of human eye fixation patterns during task-reading as classical cognitive models of human attention.We compare attention functions across two task-specific reading datasets for sentiment analysis and relation extraction.We find the predictiveness of large-scale pretrained self-attention for human attention depends on 'what is in the tail', e.g., the syntactic nature of rare contexts.Further, we observe that task-specific fine-tuning does not increase the correlation with human task-specific reading.Through an input reduction experiment we give complementary insights on the sparsity and fidelity trade-off, showing that lowerentropy attention vectors are more faithful.