Multilingual Datasets for Augmentative and Alternative Communication

Keith Vertanen

Proceedings of the 6th Workshop on Multilingual Representation Learning (MRL 2026), 2026.

There is a lack of text resources for building predictive Augmentative and Alternative Communication (AAC) interfaces in many languages. We share a compilation of four multilingual datasets in hopes of improving the situation. We started with four English datasets designed to match the conversational-style text AAC users may want to produce in person-to-person conversations. Next, we used a large language model (LLM) to correct errors and augment the original English data. Finally, we used Google's translation LLM to convert the datasets' 9,154 individual communications and 37,486 dialogue turns into 33 other languages.