# UIT-ISE-NLP at SemEval-2021 Task 5: Toxic Spans Detection with BiLSTM-CRF and ToxicBERT Comment Classification

arXiv: https://arxiv.org/abs/2104.10100 · HTML: https://mlsanity.com/paper/2104.10100/

## 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 (most cited first, 8 of 8)

- [Automated Hate Speech Detection and the Problem of Offensive Language](https://mlsanity.com/paper/1703.04009/) (2017)
- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [Predicting the Type and Target of Offensive Posts in Social Media](https://mlsanity.com/paper/1902.09666/) (2019)
- [Deep Learning for Hate Speech Detection in Tweets](https://mlsanity.com/paper/1706.00188/) (2017)
- [Fine-Grained Analysis of Propaganda in News Articles](https://mlsanity.com/paper/1910.02517/) (2019)
- [CONAN -- COunter NArratives through Nichesourcing: a Multilingual Dataset of Responses to Fight Online Hate Speech](https://mlsanity.com/paper/1910.03270/) (2019)
- [CharBERT: Character-aware Pre-trained Language Model](https://mlsanity.com/paper/2011.01513/) (2020)
- [HSD Shared Task in VLSP Campaign 2019:Hate Speech Detection for Social Good](https://mlsanity.com/paper/2007.06493/) (2020)

## Built on by (most cited first, 3 of 3)

- [Offensive Language Identification in Low-resourced Code-mixed Dravidian languages using Pseudo-labeling](https://mlsanity.com/paper/2108.12177/) (2021)
- [Enhancing LLM-based Hatred and Toxicity Detection with Meta-Toxic Knowledge Graph](https://mlsanity.com/paper/2412.15268/) (2024)
- [Adversarial and Safely Scaled Question Generation](https://mlsanity.com/paper/2210.09467/) (2022)

## 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.