Year: 2026
Type: conference-paper
Abstract: We present PICTOEDUCA, the first large-scale Spanish text-to-pictogram dataset for augmentative and alternative communication (AAC), derived from primary educational materials and grounded in the ARASAAC pictogram repository.The dataset is released with a reproducible pipeline that combines automatic annotation with targeted expert correction, supporting scalable and high-quality corpus construction.We benchmark a rule-based system (ARAWORD) and neural models (T5, LLaMA) under direct text-to-pictogram and two-stage text-toconcept-to-pictogram settings.Results show that the rule-based system remains a strong baseline, while neural models benefit from explicit semantic abstraction, with the two-stage approach improving semantic coherence and reducing ambiguity.We further explore data selection strategies, demonstrating that combining domain similarity with a quality signal yields higher-quality silver data, reduces annotation effort, and improves model performance in low-resource regimes.PICTOEDUCA enables reproducible evaluation and advances Spanish text-to-pictogram research.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W7166848935