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.
@article{arxiv.2104.01566,
title = {IITK@Detox at SemEval-2021 Task 5: Semi-Supervised Learning and Dice Loss for Toxic Spans Detection},
author = {Archit Bansal and Abhay Kaushik and Ashutosh Modi},
journal= {arXiv preprint arXiv:2104.01566},
year = {2021}
}
Comments
Accepted at SemEval 2021 Task 5, 9 Pages (6 Pages main content + 1 Page for references + 2 Pages Appendix)