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Eye movements during reading offer insights into both the reader's cognitiveprocesses and the characteristics of the text that is being read. Hence, theanalysis of scanpaths in reading have attracted increasing attention acrossfields, ranging from cognitive science over linguistics to computer science. Inparticular, eye-tracking-while-reading data has been argued to bear thepotential to make machine-learning-based language models exhibit a morehuman-like linguistic behavior. However, one of the main challenges in modelinghuman scanpaths in reading is their dual-sequence nature: the words are orderedfollowing the grammatical rules of the language, whereas the fixations arechronologically ordered. As humans do not strictly read from left-to-right, butrather skip or refixate words and regress to previous words, the alignment ofthe linguistic and the temporal sequence is non-trivial. In this paper, wedevelop Eyettention, the first dual-sequence model that simultaneouslyprocesses the sequence of words and the chronological sequence of fixations.The alignment of the two sequences is achieved by a cross-sequence attentionmechanism. We show that Eyettention outperforms state-of-the-art models inpredicting scanpaths. We provide an extensive within- and across-data setevaluation on different languages. An ablation study and qualitative analysissupport an in-depth understanding of the model's behavior.

Eyettention: An Attention-based Dual-Sequence Model for Predicting Human Scanpaths during Reading