Predictive Authoring for Brazilian Portuguese Augmentative and
  Alternative Communication
Jayr Pereira Rodrigo Nogueira Cleber Zanchettin Robson Fidalgo
Individuals with complex communication needs (CCN) often rely on augmentative
and alternative communication (AAC) systems to have conversations and
communique their wants. Such systems allow message authoring by arranging
pictograms in sequence. However, the difficulty of finding the desired item to
complete a sentence can increase as the user's vocabulary increases. This paper
proposes using BERTimbau, a Brazilian Portuguese version of BERT, for pictogram
prediction in AAC systems. To finetune BERTimbau, we constructed an AAC corpus
for Brazilian Portuguese to use as a training corpus. We tested different
approaches to representing a pictogram for prediction: as a word (using
pictogram captions), as a concept (using a dictionary definition), and as a set
of synonyms (using related terms). We also evaluated the usage of images for
pictogram prediction. The results demonstrate that using embeddings computed
from the pictograms' caption, synonyms, or definitions have a similar
performance. Using synonyms leads to lower perplexity, but using captions leads
to the highest accuracies. This paper provides insight into how to represent a
pictogram for prediction using a BERT-like model and the potential of using
images for pictogram prediction.