Abstract
In this work, we present our approach and findings for SemEval-2021 Task 5 - Toxic Spans Detection. The task’s main aim was to identify spans to which a given text’s toxicity could be attributed. The task is challenging mainly due to two constraints: the small training dataset and imbalanced class distribution. Our paper investigates two techniques, semi-supervised learning and learning with Self-Adjusting Dice Loss, for tackling these challenges. Our submitted system (ranked ninth on the leader board) consisted of an ensemble of various pre-trained Transformer Language Models trained using either of the above-proposed techniques.- Anthology ID:
- 2021.semeval-1.24
- 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:
- 211–219
- Language:
- URL:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2021.semeval-1.24/
- DOI:
- 10.18653/v1/2021.semeval-1.24
- Cite (ACL):
- Archit Bansal, Abhay Kaushik, and Ashutosh Modi. 2021. IITK@Detox at SemEval-2021 Task 5: Semi-Supervised Learning and Dice Loss for Toxic Spans Detection. In Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), pages 211–219, Online. Association for Computational Linguistics.
- Cite (Informal):
- IITK@Detox at SemEval-2021 Task 5: Semi-Supervised Learning and Dice Loss for Toxic Spans Detection (Bansal et al., SemEval 2021)
- PDF:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2021.semeval-1.24.pdf
- Code
- architb1703/Toxic_Span
- Data
- Civil Comments