# Cross-Domain Toxic Spans Detection

arXiv: https://arxiv.org/abs/2306.09642 · HTML: https://mlsanity.com/paper/2306.09642/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 24 references that are in the map.
- First posted on arXiv 16 Jun 2023.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1704.02685/) (2017)
- [HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection](https://mlsanity.com/paper/2012.10289/) (2020)
- [Challenges for Toxic Comment Classification: An In-Depth Error Analysis](https://mlsanity.com/paper/1809.07572/) (2018)
- [MUDES: Multilingual Detection of Offensive Spans](https://mlsanity.com/paper/2102.09665/) (2021)
- [NLRG at SemEval-2021 Task 5: Toxic Spans Detection Leveraging BERT-based Token Classification and Span Prediction Techniques](https://mlsanity.com/paper/2102.12254/) (2021)

## Built on by (most cited first, 4 of 4)

- [The Constant in HATE: Analyzing Toxicity in Reddit across Topics and Languages](https://mlsanity.com/paper/2404.18726/) (2024)
- [Grounding Toxicity in Real-World Events across Languages](https://mlsanity.com/paper/2405.13754/) (2024)
- [Combating Toxic Language: A Review of LLM-Based Strategies for Software Engineering](https://mlsanity.com/paper/2504.15439/) (2025)
- [Assessing and Refining ChatGPT's Performance in Identifying Targeting and Inappropriate Language: A Comparative Study](https://mlsanity.com/paper/2505.21710/) (2025)

## Abstract (arXiv)

Given the dynamic nature of toxic language use, automated methods for detecting toxic spans are likely to encounter distributional shift. To explore this phenomenon, we evaluate three approaches for detecting toxic spans under cross-domain conditions: lexicon-based, rationale extraction, and fine-tuned language models. Our findings indicate that a simple method using off-the-shelf lexicons performs best in the cross-domain setup. The cross-domain error analysis suggests that (1) rationale extraction methods are prone to false negatives, while (2) language models, despite performing best for the in-domain case, recall fewer explicitly toxic words than lexicons and are prone to certain types of false positives. Our code is publicly available at: https://github.com/sfschouten/toxic-cross-domain.