Enhancing Augmentative and Alternative Communication with Card Prediction and Colourful Semantics
It addresses communication challenges for individuals with complex communication needs, but is incremental as it adapts existing methods to a specific language and framework.
This paper tackled the problem of improving Augmentative and Alternative Communication (AAC) systems for Brazilian Portuguese by integrating Colourful Semantics (CS) with a transformer-based model, resulting in BERTptCS significantly outperforming a baseline model in metrics like top-k accuracy and Mean Reciprocal Rank.
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.