UIT-ISE-NLP at SemEval-2021 Task 5: Toxic Spans Detection with BiLSTM-CRF and ToxicBERT Comment Classification
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Abstract (arXiv)
We present our works on SemEval-2021 Task 5 about Toxic Spans Detection. This task aims to build a model for identifying toxic words in whole posts. We use the BiLSTM-CRF model combining with ToxicBERT Classification to train the detection model for identifying toxic words in posts. Our model achieves 62.23% by F1-score on the Toxic Spans Detection task.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 8 of its 24 references that are in the map. First posted on arXiv 20 Apr 2021.
Builds on
8 of 24 references.
References · 8
- 2017Automated Hate Speech Detection and the Problem of Offensive Languagecited by 736
- 2016Neural Architectures for Named Entity Recognitioncited by 550
- 2019Predicting the Type and Target of Offensive Posts in Social Mediacited by 271
- 2017Deep Learning for Hate Speech Detection in Tweetscited by 180
- 2019Fine-Grained Analysis of Propaganda in News Articlescited by 133
- 2019CONAN -- COunter NArratives through Nichesourcing: a Multilingual Dataset of Responses to Fight Online Hate Speechcited by 110
- 2020CharBERT: Character-aware Pre-trained Language Modelcited by 63
- 2020HSD Shared Task in VLSP Campaign 2019:Hate Speech Detection for Social Goodcited by 11
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Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 1.
3 papers.