UIT-E10dot3 at SemEval-2021 Task 5: Toxic Spans Detection with Named Entity Recognition and Question-Answering Approaches

Phu Gia Hoang, Luan Thanh Nguyen, Kiet Nguyen


Abstract
The increment of toxic comments on online space is causing tremendous effects on other vulnerable users. For this reason, considerable efforts are made to deal with this, and SemEval-2021 Task 5: Toxic Spans Detection is one of those. This task asks competitors to extract spans that have toxicity from the given texts, and we have done several analyses to understand its structure before doing experiments. We solve this task by two approaches, Named Entity Recognition with spaCy’s library and Question-Answering with RoBERTa combining with ToxicBERT, and the former gains the highest F1-score of 66.99%.
Anthology ID:
2021.semeval-1.125
Volume:
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
Month:
August
Year:
2021
Address:
Online
Venue:
SemEval
SIGs:
SIGSEM | SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
919–926
Language:
URL:
https://aclanthology.org/2021.semeval-1.125
DOI:
10.18653/v1/2021.semeval-1.125
Bibkey:
Cite (ACL):
Phu Gia Hoang, Luan Thanh Nguyen, and Kiet Nguyen. 2021. UIT-E10dot3 at SemEval-2021 Task 5: Toxic Spans Detection with Named Entity Recognition and Question-Answering Approaches. In Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), pages 919–926, Online. Association for Computational Linguistics.
Cite (Informal):
UIT-E10dot3 at SemEval-2021 Task 5: Toxic Spans Detection with Named Entity Recognition and Question-Answering Approaches (Gia Hoang et al., SemEval 2021)
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PDF:
https://aclanthology.org/2021.semeval-1.125.pdf