Aurélie Calabrèse

Alexandre Bonlarron, Aurélie Calabrèse, Pierre Kornprobst et al.

This paper introduces a new approach to generating strongly constrained texts. We consider standardized sentence generation for the typical application of vision screening. To solve this problem, we formalize it as a discrete combinatorial optimization problem and utilize multivalued decision diagrams (MDD), a well-known data structure to deal with constraints. In our context, one key strength of MDD is to compute an exhaustive set of solutions without performing any search. Once the sentences are obtained, we apply a language model (GPT-2) to keep the best ones. We detail this for English and also for French where the agreement and conjugation rules are known to be more complex. Finally, with the help of GPT-2, we get hundreds of bona-fide candidate sentences. When compared with the few dozen sentences usually available in the well-known vision screening test (MNREAD), this brings a major breakthrough in the field of standardized sentence generation. Also, as it can be easily adapted for other languages, it has the potential to make the MNREAD test even more valuable and usable. More generally, this paper highlights MDD as a convincing alternative for constrained text generation, especially when the constraints are hard to satisfy, but also for many other prospects.

Sebastián Gallardo, Hui-Yin Wu, Dorian Mazauric et al.

Understanding how diverse audiences engage with structured media is critical to ensure a consistent quality of experience. In this context, we quantify the behavioral and performance cost of manual navigation (e.g., pinch and zoom) versus direct structural access in layout-based digital documents. We specifically investigate newspaper reading when visual access to structural cues (headlines as entry points) is constrained. Participants completed two tasks-reading all headlines aloud and locating target articles-under two conditions: (1) original edition with gesture-based magnification (pan and zoom), which is the industry standard for digital documents, and (2) large-print edition supporting direct-access reading. We collected performance measures (success ratio and completion time), behavioral integrity through reading path analysis, alongside perceived workload and preferences (NASA-TLX). Results from linear mixed-effects models show that the large-print condition yielded not only better performance than gesture-based magnification (18% improvement in reading speed, 30% improvement in speed to locate a target), but more importantly, restored the natural reading strategy that gesture-based magnification interaction disrupts. Readers also reported lower workload and higher preference. These findings highlight the importance of developing automated methods for generating large-print editions, where layout adaptation complements font scaling to support accessibility and quality of experience.