Social network platforms are generally used to share positive, constructive,
and insightful content. However, in recent times, people often get exposed to
objectionable content like threat, identity attacks, hate speech, insults,
obscene texts, offensive remarks or bullying. Existing work on toxic speech
detection focuses on binary classification or on differentiating toxic speech
among a small set of categories. This paper describes the system proposed by
team Cisco for SemEval-2021 Task 5: Toxic Spans Detection, the first shared
task focusing on detecting the spans in the text that attribute to its
toxicity, in English language. We approach this problem primarily in two ways:
a sequence tagging approach and a dependency parsing approach. In our sequence
tagging approach we tag each token in a sentence under a particular tagging
scheme. Our best performing architecture in this approach also proved to be our
best performing architecture overall with an F1 score of 0.6922, thereby
placing us 7th on the final evaluation phase leaderboard. We also explore a
dependency parsing approach where we extract spans from the input sentence
under the supervision of target span boundaries and rank our spans using a
biaffine model. Finally, we also provide a detailed analysis of our results and
model performance in our paper.

该论文描述了 Cisco 团队提出的用于发现英文文本中的有毒段落的系统，该系统主要采用了序列标注和依赖解析方法，并提供了其结果和模型表现的详细分析。

2021 年 SemEval 第 5 任务：毒性在哪里？：利用 Transformer 从在线评论中提取多个毒性段

Cisco at SemEval-2021 Task 5: What's Toxic?: Leveraging Transformers for  Multiple Toxic Span Extraction from Online Comments

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。