面向巴西葡萄牙语的预测作者生成
Predictive Authoring for Brazilian Portuguese Augmentative and Alternative Communication
AI总结：
本文提出使用BERTimbau模型提升巴西葡萄牙语AAC系统中图标预测的准确性，通过不同表示方法对比发现同义词预测效果最佳。
摘要：
具有复杂沟通需求（CCN）的个体常常依赖于增强型和替代型沟通（AAC）系统来进行交流和表达他们的需求。此类系统允许通过按顺序排列图标来编写信息。然而，随着用户词汇量的增加，找到所需物品来完成句子的难度也会增加。本文提出使用BERTimbau，即BERT的巴西葡萄牙语版本，用于AAC系统中的图标预测。为了微调BERTimbau，我们构建了一个用于巴西葡萄牙语的AAC语料库作为训练语料。我们测试了不同方法来表示图标以进行预测：作为单词（使用图标说明词）、作为概念（使用字典定义）以及作为一组同义词（使用相关术语）。我们还评估了使用图像进行图标预测的使用情况。结果表明，使用从图标说明词、同义词或定义中计算出的嵌入具有相似的性能。使用同义词会导致较低的困惑度，但使用说明词会导致最高的准确性。本文提供了如何使用BERT-like模型表示图标以进行预测的见解，以及使用图像进行图标预测的潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
