# GUIR at SemEval-2020 Task 12: Domain-Tuned Contextualized Models for Offensive Language Detection

arXiv: https://arxiv.org/abs/2007.14477 · HTML: https://mlsanity.com/paper/2007.14477/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jul 2020.

## Built on by (most cited first, 6 of 6)

- [ToxCCIn: Toxic Content Classification with Interpretability](https://mlsanity.com/paper/2103.01328/) (2021)
- [TLDR9+: A Large Scale Resource for Extreme Summarization of Social Media Posts](https://mlsanity.com/paper/2110.01159/) (2021)
- [Offensive Language Detection with BERT-based models, By Customizing Attention Probabilities](https://mlsanity.com/paper/2110.05133/) (2021)
- [Concept-Based Interpretability for Toxicity Detection](https://mlsanity.com/paper/2511.16689/) (2025)
- [Neural Models for Offensive Language Detection](https://mlsanity.com/paper/2106.14609/) (2021)
- [Sentiment Analysis in SemEval: A Review of Sentiment Identification Approaches](https://mlsanity.com/paper/2503.10457/) (2025)

## Abstract (arXiv)

Offensive language detection is an important and challenging task in natural language processing. We present our submissions to the OffensEval 2020 shared task, which includes three English sub-tasks: identifying the presence of offensive language (Sub-task A), identifying the presence of target in offensive language (Sub-task B), and identifying the categories of the target (Sub-task C). Our experiments explore using a domain-tuned contextualized language model (namely, BERT) for this task. We also experiment with different components and configurations (e.g., a multi-view SVM) stacked upon BERT models for specific sub-tasks. Our submissions achieve F1 scores of 91.7% in Sub-task A, 66.5% in Sub-task B, and 63.2% in Sub-task C. We perform an ablation study which reveals that domain tuning considerably improves the classification performance. Furthermore, error analysis shows common misclassification errors made by our model and outlines research directions for future.