Aspect-based sentiment analysis has been studied in both research and industrial communities over recent years. For the low-resource languages, the standard benchmark corpora play an important role in the development of methods. In this article, we introduce two benchmark corpora with the largest sizes at sentence-level for two tasks: Aspect Category Detection and Aspect Polarity Classification in Vietnamese. Our corpora are annotated with high inter-annotator agreements for the restaurant and hotel domains. The release of our corpora would push forward the low-resource language processing community. In addition, we deploy and compare the effectiveness of supervised learning methods with a single and multi-task approach based on deep learning architectures. Experimental results on our corpora show that the multi-task approach based on BERT architecture outperforms the neural network architectures and the single approach. Our corpora and source code are published on this footnoted site. 1

中文翻译：

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两个新的大型越南语体句级情感分析语料库

近年来，研究和工业界都对基于方面的情感分析进行了研究。对于低资源语言，标准基准语料库在方法开发中发挥着重要作用。在本文中，我们介绍了两个在句子级别具有最大尺寸的基准语料库，用于两个任务：Aspect Category Detection 和 Aspect Polarity Classification in Vietnam。我们的语料库使用餐厅和酒店领域的高注释者间协议进行了注释。我们的语料库的发布将推动低资源语言处理社区的发展。此外，我们部署并比较了监督学习方法与基于深度学习架构的单任务和多任务方法的有效性。在我们的语料库上的实验结果表明，基于 BERT 架构的多任务方法优于神经网络架构和单一方法。我们的语料库和源代码发布在这个带脚注的网站上。1