  The real-world impact of polarization and toxicity in the online sphere
marked the end of 2020 and the beginning of this year in a negative way.
Semeval-2021, Task 5 - Toxic Spans Detection is based on a novel annotation of
a subset of the Jigsaw Unintended Bias dataset and is the first language
toxicity detection task dedicated to identifying the toxicity-level spans. For
this task, participants had to automatically detect character spans in short
comments that render the message as toxic. Our model considers applying Virtual
Adversarial Training in a semi-supervised setting during the fine-tuning
process of several Transformer-based models (i.e., BERT and RoBERTa), in
combination with Conditional Random Fields. Our approach leads to performance
improvements and more robust models, enabling us to achieve an F1-score of
65.73% in the official submission and an F1-score of 66.13% after further
tuning during post-evaluation.
