Accurately predicting human scanpaths duringreading is vital for diverse fields and downstream tasks, from educational technologies toautomatic question answering. To date, however, progress in this direction remains limited by scarce gaze data. We overcome theissue with ScanEZ, a self-supervised framework grounded in cognitive models of reading.ScanEZ jointly models the spatial and temporal dimensions of scanpaths by leveraging synthetic data and a 3-D gaze objective inspired bymasked language modeling. With this framework, we provide evidence that two key factorsin scanpath prediction during reading are: theuse of masked modeling of both spatial andtemporal patterns of eye movements, and cognitive model simulations as an inductive biasto kick-start training. Our approach achievesstate-of-the-art results on established datasets(e.g., up to 31.4% negative log-likelihood improvement on CELER L1), and proves portableacross different experimental conditions.

Recent advances in NLP have led to a rise in inter-disciplinary and application-oriented research. While this demonstrates the growing real-world impact of the field, research papers frequently feature experiments that do not account for the complexities of realistic data and environments. To explore the extent of this gap, we investigate the relationship between the real-world motivations described in NLP papers and the models and evaluation which comprise the proposed solution. We first survey papers from the NLP Applications track from ACL 2020 and EMNLP 2020, asking which papers have differences between their stated motivation and their experimental setting, and if so, mention them. We find that many papers fall short of considering real-world input and output conditions due to adopting simplified modeling or evaluation settings. As a case study, we then empirically show that the performance of an educational dialog understanding system deteriorates when used in a realistic classroom environment.