WLV-RIT at SemEval-2021 Task 5: A Neural Transformer Framework for Detecting Toxic Spans

by   Tharindu Ranasinghe, et al.

In recent years, the widespread use of social media has led to an increase in the generation of toxic and offensive content on online platforms. In response, social media platforms have worked on developing automatic detection methods and employing human moderators to cope with this deluge of offensive content. While various state-of-the-art statistical models have been applied to detect toxic posts, there are only a few studies that focus on detecting the words or expressions that make a post offensive. This motivates the organization of the SemEval-2021 Task 5: Toxic Spans Detection competition, which has provided participants with a dataset containing toxic spans annotation in English posts. In this paper, we present the WLV-RIT entry for the SemEval-2021 Task 5. Our best performing neural transformer model achieves an 0.68 F1-Score. Furthermore, we develop an open-source framework for multilingual detection of offensive spans, i.e., MUDES, based on neural transformers that detect toxic spans in texts.


BRUMS at SemEval-2020 Task 12 : Transformer based Multilingual Offensive Language Identification in Social Media

In this paper, we describe the team BRUMS entry to OffensEval 2: Multili...

Detecting Abusive Albanian

The ever growing usage of social media in the recent years has had a dir...

Automatic Detection of Cyberbullying in Social Media Text

While social media offer great communication opportunities, they also in...

TweepFake: about Detecting Deepfake Tweets

The threat of deepfakes, synthetic, or manipulated media, is becoming in...

Understanding and Detecting Hateful Content using Contrastive Learning

The spread of hate speech and hateful imagery on the Web is a significan...

Automatic identification of suicide notes with a transformer-based deep learning model

Suicide is one of the leading causes of death worldwide. At the same tim...

MUDES: Multilingual Detection of Offensive Spans

The interest in offensive content identification in social media has gro...