The reviews of customers play an essential role in online shopping. People
often refer to reviews or comments of previous customers to decide whether to
buy a new product. Catching up with this behavior, some people create untruths
and illegitimate reviews to hoax customers about the fake quality of products.
These are called spam reviews, confusing consumers on online shopping platforms
and negatively affecting online shopping behaviors. We propose the dataset
called ViSpamReviews, which has a strict annotation procedure for detecting
spam reviews on e-commerce platforms. Our dataset consists of two tasks: the
binary classification task for detecting whether a review is spam or not and
the multi-class classification task for identifying the type of spam. The
PhoBERT obtained the highest results on both tasks, 86.89% and 72.17%,
respectively, by macro average F1 score.

本文提出了一个叫做 ViSpamReviews 的数据集，用于检测电子商务平台上的垃圾评论，包含两个任务，一个是二分类任务，用于判断评论是否为垃圾评论，另一个是多分类任务，用于识别垃圾评论的类型。 PhoBERT 在两个任务中都表现最好，分别获得了 86.89％和 72.17％的宏平均 F1 分数。

检测越南电子商务网站上的垃圾评论

Detecting Spam Reviews on Vietnamese E-commerce Websites

We present PhoBERT with two versions, PhoBERT-base and PhoBERT-large, the
first public large-scale monolingual language models pre-trained for
Vietnamese. Experimental results show that PhoBERT consistently outperforms the
recent best pre-trained multilingual model XLM-R (Conneau et al., 2020) and
improves the state-of-the-art in multiple Vietnamese-specific NLP tasks
including Part-of-speech tagging, Dependency parsing, Named-entity recognition
and Natural language inference. We release PhoBERT to facilitate future
research and downstream applications for Vietnamese NLP. Our PhoBERT models are
available at this https URL

PhoBERT 是第一个用于越南语的公开大规模单语言模型，实验结果表明，PhoBERT 在多个越南特定的自然语言处理任务中表现优异，胜过最近最好的预训练多语种模型 XLM-R，我们发布了 PhoBERT 以促进未来的越南语自然语言处理研究和下游应用。