  This paper presents an approach to enhancing Augmentative and Alternative
Communication (AAC) systems by integrating Colourful Semantics (CS) with
transformer-based language models specifically tailored for Brazilian
Portuguese. We introduce an adapted BERT model, BERTptCS, which incorporates
the CS framework for improved prediction of communication cards. The primary
aim is to enhance the accuracy and contextual relevance of communication card
predictions, which are essential in AAC systems for individuals with complex
communication needs (CCN). We compared BERTptCS with a baseline model,
BERTptAAC, which lacks CS integration. Our results demonstrate that BERTptCS
significantly outperforms BERTptAAC in various metrics, including top-k
accuracy, Mean Reciprocal Rank (MRR), and Entropy@K. Integrating CS into the
language model improves prediction accuracy and offers a more intuitive and
contextual understanding of user inputs, facilitating more effective
communication.
