The rise of social media has led to the increasing of comments on online
forums. However, there still exists invalid comments which are not informative
for users. Moreover, those comments are also quite toxic and harmful to people.
In this paper, we create a dataset for constructive and toxic speech detection,
named UIT-ViCTSD (Vietnamese Constructive and Toxic Speech Detection dataset)
with 10,000 human-annotated comments. For these tasks, we propose a system for
constructive and toxic speech detection with the state-of-the-art transfer
learning model in Vietnamese NLP as PhoBERT. With this system, we obtain
F1-scores of 78.59% and 59.40% for classifying constructive and toxic comments,
respectively. Besides, we implement various baseline models as traditional
Machine Learning and Deep Neural Network-Based models to evaluate the dataset.
With the results, we can solve several tasks on the online discussions and
develop the framework for identifying constructiveness and toxicity of
Vietnamese social media comments automatically.

在本文中，我们使用越南自然语言处理中的 PhoBERT 等最先进的迁移学习模型，提出了一个用于建设性和有毒言论检测的系统，并使用 10000 个人工注释的评论创建了一个名为 UIT-ViCTSD 的数据集，从而解决了社交媒体评论质量低下和有害性问题。

越南开放式社交媒体评论的建设性和有毒言论检测

Constructive and Toxic Speech Detection for Open-domain Social Media  Comments in Vietnamese

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。