Omer Shubi

With the rise of LLMs, NLP has been redefining itself as a field of study, with an increasing interest shift towards areas such as NLP for science, model analysis, human-centered AI and user-facing applications. As part of this transformation, in recent years, a new area of NLP which combines text processing with eye movements in reading has been emerging. Recent work has demonstrated that this area has a strong potential for driving innovation in NLP and enhancing its scientific and societal impact. In this survey, we characterize the main streams of work on the intersection of eye tracking for reading and NLP, provide critical commentary on promises and challenges, and identify key open directions that can guide future work in this area.

We present OneStop Eye Movements, a large-scale corpus of eye movements in reading, in which native (L1) speakers read newswire texts in English and answer reading comprehension questions. OneStop has 152 hours of eye movement recordings from 360 participants for 2.6 million word tokens, more data than all the existing public broad coverage English L1 eye tracking datasets combined. The eye movement data was collected for extensively piloted reading comprehension materials comprising 486 reading comprehension questions and auxiliary text annotations geared towards behavioral analyses of reading comprehension. Importantly, OneStop includes multiple reading regimes: ordinary reading, information seeking, repeated reading of the same text, and reading simplified text. The combination of the unprecedented size, high-quality reading comprehension materials and multiple reading scenarios, aims to enable new research avenues in the study of reading and human language processing. It further aims to facilitate the integration of eye tracking data in Natural Language Processing (NLP), Artificial Intelligence (AI), Human Computer Interaction (HCI) and educational applications.

Text simplification is a common practice for making texts easier to read and easier to understand. To which extent does it achieve these goals, and which participant and text characteristics drive simplification benefits? In this work, we use eye tracking to address these questions for the first time for the population of adult native (L1) English speakers. We find that 42% of the readers exhibit reading facilitation effects, while only 2% improve reading comprehension accuracy. We further observe that reading fluency benefits are larger for slower and lessexperienced readers, while comprehension benefits are more substantial in lower comprehension readers, but not vice versa. Finally, we find that high-complexity original texts are key forenhancing reading fluency, while large complexity reduction is more pertinent to improving comprehension. Our study highlights the potential of cognitive measures in the evaluation of text simplification and distills empirically driven principles for enhancing simplification effectiveness.

We present OneStop Eye Movements, a large-scale corpus of eye movements in reading, in which native speakers read newswire texts in English and answer reading comprehension questions. OneStop has 152 hours of passage reading eye movement recordings from 360 participants for 2.6 million word tokens, more data than all the existing public broad coverage native English eye tracking datasets combined. The eye movement data was collected for extensively piloted reading comprehension materials comprising 486 reading comprehension questions and auxiliary text annotations geared towards behavioral analyses of reading comprehension. OneStop includes multiple reading regimes: ordinary reading, information seeking, repeated reading of the same text, and reading simplified text. The combination of the unprecedented size, high-quality reading comprehension materials and multiple reading regimes, aims to enable new research avenues in the study of reading and human language processing. It further aims to facilitate the integration of eye tracking data in Natural Language Processing (NLP), Artificial Intelligence (AI), Human Computer Interaction (HCI) and educational applications.