110 research outputs found
SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020)
We present the results and main findings of SemEval-2020 Task 12 on
Multilingual Offensive Language Identification in Social Media (OffensEval
2020). The task involves three subtasks corresponding to the hierarchical
taxonomy of the OLID schema (Zampieri et al., 2019a) from OffensEval 2019. The
task featured five languages: English, Arabic, Danish, Greek, and Turkish for
Subtask A. In addition, English also featured Subtasks B and C. OffensEval 2020
was one of the most popular tasks at SemEval-2020 attracting a large number of
participants across all subtasks and also across all languages. A total of 528
teams signed up to participate in the task, 145 teams submitted systems during
the evaluation period, and 70 submitted system description papers.Comment: Proceedings of the International Workshop on Semantic Evaluation
(SemEval-2020
Towards Automated Moderation: Enabling Toxic Language Detection with Transfer Learning and Attention-Based Models
Our world is more connected than ever before. Sadly, however, this highly connected world has made it easier to bully, insult, and propagate hate speech on the cyberspace. Even though researchers and companies alike have started investigating this real-world problem, the question remains as to why users are increasingly being exposed to hate and discrimination online. In fact, the noticeable and persistent increase in harmful language on social media platforms indicates that the situation is, actually, only getting worse. Hence, in this work, we show that contemporary ML methods can help tackle this challenge in an accurate and cost-effective manner. Our experiments demonstrate that a universal approach combining transfer learning methods and state-of-the-art Transformer architectures can trigger the efficient development of toxic language detection models. Consequently, with this universal approach, we provide platform providers with a simplistic approach capable of enabling the automated moderation of user-generated content, and as a result, hope to contribute to making the web a safer place
Detecting Abusive Language on Online Platforms: A Critical Analysis
Abusive language on online platforms is a major societal problem, often
leading to important societal problems such as the marginalisation of
underrepresented minorities. There are many different forms of abusive language
such as hate speech, profanity, and cyber-bullying, and online platforms seek
to moderate it in order to limit societal harm, to comply with legislation, and
to create a more inclusive environment for their users. Within the field of
Natural Language Processing, researchers have developed different methods for
automatically detecting abusive language, often focusing on specific
subproblems or on narrow communities, as what is considered abusive language
very much differs by context. We argue that there is currently a dichotomy
between what types of abusive language online platforms seek to curb, and what
research efforts there are to automatically detect abusive language. We thus
survey existing methods as well as content moderation policies by online
platforms in this light, and we suggest directions for future work
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