论文标题
帕斯伯特：基于变压器的波斯语言理解的模型
ParsBERT: Transformer-based Model for Persian Language Understanding
论文作者
论文摘要
预先训练的语言模型的激增已经开始在自然语言处理领域（NLP）的新时代，允许我们构建强大的语言模型。在这些模型中，由于其最新性能，基于变压器的模型（例如BERT）变得越来越流行。但是，这些模型通常专注于英语，将其他语言留给具有有限资源的多语言模型。本文提出了波斯语（Parsbert）的单语伯特（Parsbert），该文字显示了与其他建筑和多语言模型相比的最先进的性能。同样，由于波斯语中NLP任务的可用数据量非常受限制，因此组成了用于不同NLP任务的大量数据集以及模型预先培训。帕斯伯特（Parsbert）在所有数据集中获得了更高的分数，包括现有数据集以及组成的分数，并通过在情感分析，文本分类和命名实体识别任务中胜过多种语言BERT和其他先前的作品，从而提高了最先进的性能。
The surge of pre-trained language models has begun a new era in the field of Natural Language Processing (NLP) by allowing us to build powerful language models. Among these models, Transformer-based models such as BERT have become increasingly popular due to their state-of-the-art performance. However, these models are usually focused on English, leaving other languages to multilingual models with limited resources. This paper proposes a monolingual BERT for the Persian language (ParsBERT), which shows its state-of-the-art performance compared to other architectures and multilingual models. Also, since the amount of data available for NLP tasks in Persian is very restricted, a massive dataset for different NLP tasks as well as pre-training the model is composed. ParsBERT obtains higher scores in all datasets, including existing ones as well as composed ones and improves the state-of-the-art performance by outperforming both multilingual BERT and other prior works in Sentiment Analysis, Text Classification and Named Entity Recognition tasks.
