Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.15896 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913362477056000 |
|---|---|
| author | Pereira, Jayr Rodrigues, Francisco Pereira, Jaylton Zanchettin, Cleber Fidalgo, Robson |
| author_facet | Pereira, Jayr Rodrigues, Francisco Pereira, Jaylton Zanchettin, Cleber Fidalgo, Robson |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_15896 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing Augmentative and Alternative Communication with Card Prediction and Colourful Semantics Pereira, Jayr Rodrigues, Francisco Pereira, Jaylton Zanchettin, Cleber Fidalgo, Robson Computation and Language 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. |
| title | Enhancing Augmentative and Alternative Communication with Card Prediction and Colourful Semantics |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2405.15896 |