Abstract
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.- Anthology ID:
- 2021.semeval-1.26
- Volume:
- Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Editors:
- Alexis Palmer, Nathan Schneider, Natalie Schluter, Guy Emerson, Aurelie Herbelot, Xiaodan Zhu
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 225–232
- Language:
- URL:
- https://aclanthology.org/2021.semeval-1.26
- DOI:
- 10.18653/v1/2021.semeval-1.26
- Cite (ACL):
- Andrei Paraschiv, Dumitru-Clementin Cercel, and Mihai Dascalu. 2021. UPB at SemEval-2021 Task 5: Virtual Adversarial Training for Toxic Spans Detection. In Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), pages 225–232, Online. Association for Computational Linguistics.
- Cite (Informal):
- UPB at SemEval-2021 Task 5: Virtual Adversarial Training for Toxic Spans Detection (Paraschiv et al., SemEval 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2021.semeval-1.26.pdf