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IITK@Detox at SemEval-2021 Task 5: Semi-Supervised Learning and Dice Loss for Toxic Spans Detection

2021-04-04 08:39:55
Archit Bansal, Abhay Kaushik, Ashutosh Modi
arXiv_CL
         
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.

Abstract (translated)

URL

https://arxiv.org/abs/2104.01566

PDF

https://arxiv.org/pdf/2104.01566.pdf

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