论文标题
单声道与基于多语言变压器的模型：几个语言任务的比较
Mono vs Multilingual Transformer-based Models: a Comparison across Several Language Tasks
论文作者
论文摘要
BERT（来自变形金刚的双向编码器表示）和Albert（Lite Bert）是训练前语言模型的方法，以后可以对其进行微调，以用于各种自然语言理解任务。这些方法已应用于许多此类任务（主要用于英语），从而实现了超出最先进的结果。在本文中，我们的贡献是双重的。首先，我们为葡萄牙语提供了经过训练的Bert和Albert模型。其次，我们使用语义文本相似性中的实验比较了单语和标准的多语言模型，识别文本需要，文本类别分类，情感分析，令人反感的评论检测和虚假新闻检测，以评估生成语言表示的有效性。结果表明，单语和多语言模型都能够实现最新的模型，并且训练单语言模型（如果有的话）的优势很小。
BERT (Bidirectional Encoder Representations from Transformers) and ALBERT (A Lite BERT) are methods for pre-training language models which can later be fine-tuned for a variety of Natural Language Understanding tasks. These methods have been applied to a number of such tasks (mostly in English), achieving results that outperform the state-of-the-art. In this paper, our contribution is twofold. First, we make available our trained BERT and Albert model for Portuguese. Second, we compare our monolingual and the standard multilingual models using experiments in semantic textual similarity, recognizing textual entailment, textual category classification, sentiment analysis, offensive comment detection, and fake news detection, to assess the effectiveness of the generated language representations. The results suggest that both monolingual and multilingual models are able to achieve state-of-the-art and the advantage of training a single language model, if any, is small.
