Cisco at SemEval-2021 Task 5: What’s Toxic?: Leveraging Transformers for Multiple Toxic Span Extraction from Online Comments

Sreyan Ghosh, Sonal Kumar


Abstract
Social network platforms are generally used to share positive, constructive, and insightful content. However, in recent times, people often get exposed to objectionable content like threat, identity attacks, hate speech, insults, obscene texts, offensive remarks or bullying. Existing work on toxic speech detection focuses on binary classification or on differentiating toxic speech among a small set of categories. This paper describes the system proposed by team Cisco for SemEval-2021 Task 5: Toxic Spans Detection, the first shared task focusing on detecting the spans in the text that attribute to its toxicity, in English language. We approach this problem primarily in two ways: a sequence tagging approach and a dependency parsing approach. In our sequence tagging approach we tag each token in a sentence under a particular tagging scheme. Our best performing architecture in this approach also proved to be our best performing architecture overall with an F1 score of 0.6922, thereby placing us 7th on the final evaluation phase leaderboard. We also explore a dependency parsing approach where we extract spans from the input sentence under the supervision of target span boundaries and rank our spans using a biaffine model. Finally, we also provide a detailed analysis of our results and model performance in our paper.
Anthology ID:
2021.semeval-1.29
Volume:
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
Month:
August
Year:
2021
Address:
Online
Venue:
SemEval
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
249–257
Language:
URL:
https://aclanthology.org/2021.semeval-1.29
DOI:
10.18653/v1/2021.semeval-1.29
Bibkey:
Cite (ACL):
Sreyan Ghosh and Sonal Kumar. 2021. Cisco at SemEval-2021 Task 5: What’s Toxic?: Leveraging Transformers for Multiple Toxic Span Extraction from Online Comments. In Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), pages 249–257, Online. Association for Computational Linguistics.
Cite (Informal):
Cisco at SemEval-2021 Task 5: What’s Toxic?: Leveraging Transformers for Multiple Toxic Span Extraction from Online Comments (Ghosh & Kumar, SemEval 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.semeval-1.29.pdf
Code
 Sreyan88/SemEval-2021-Toxic-Spans-Detection