# Cross-Cultural Transfer Learning for Chinese Offensive Language Detection arXiv: https://arxiv.org/abs/2303.17927 ยท HTML: https://mlsanity.com/paper/2303.17927/ ## Key facts - Cited by 21 papers in the map (data through 14 Oct 2026). - Builds on 19 of its 39 references that are in the map. - First posted on arXiv 31 Mar 2023. ## Builds on (most cited first, 19 of 19) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019) - [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019) - [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019) - [Automated Hate Speech Detection and the Problem of Offensive Language](https://mlsanity.com/paper/1703.04009/) (2017) - [Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detection](https://mlsanity.com/paper/2111.07997/) (2021) - [Challenges and Strategies in Cross-Cultural NLP](https://mlsanity.com/paper/2203.10020/) (2022) - [SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)](https://mlsanity.com/paper/1903.08983/) (2019) - [SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020)](https://mlsanity.com/paper/2006.07235/) (2020) - [COLD: A Benchmark for Chinese Offensive Language Detection](https://mlsanity.com/paper/2201.06025/) (2022) - [Offensive Language and Hate Speech Detection for Danish](https://mlsanity.com/paper/1908.04531/) (2019) - [Detecting Cross-Geographic Biases in Toxicity Modeling on Social Media](https://mlsanity.com/paper/2104.06999/) (2021) - [Cross-lingual Zero- and Few-shot Hate Speech Detection Utilising Frozen Transformer Language Models and AXEL](https://mlsanity.com/paper/2004.13850/) (2020) - [Multilingual Offensive Language Identification for Low-resource Languages](https://mlsanity.com/paper/2105.05996/) (2021) - [Hate Speech and Offensive Language Detection in Bengali](https://mlsanity.com/paper/2210.03479/) (2022) - [KOLD: Korean Offensive Language Dataset](https://mlsanity.com/paper/2205.11315/) (2022) - [On The Robustness of Offensive Language Classifiers](https://mlsanity.com/paper/2203.11331/) (2022) - [Data-Efficient Strategies for Expanding Hate Speech Detection into Under-Resourced Languages](https://mlsanity.com/paper/2210.11359/) (2022) - [Measuring Geographic Performance Disparities of Offensive Language Classifiers](https://mlsanity.com/paper/2209.07353/) (2022) ## Built on by (most cited first, 21 of 21) - [Towards Measuring and Modeling "Culture" in LLMs: A Survey](https://mlsanity.com/paper/2403.15412/) (2024) - [Culture is Not Trivia: Sociocultural Theory for Cultural NLP](https://mlsanity.com/paper/2502.12057/) (2025) - [ToxiCloakCN: Evaluating Robustness of Offensive Language Detection in Chinese with Cloaking Perturbations](https://mlsanity.com/paper/2406.12223/) (2024) - [Culturally Aware and Adapted NLP: A Taxonomy and a Survey of the State of the Art](https://mlsanity.com/paper/2406.03930/) (2024) - [SeeGULL Multilingual: a Dataset of Geo-Culturally Situated Stereotypes](https://mlsanity.com/paper/2403.05696/) (2024) - [Cultural Commonsense Knowledge for Intercultural Dialogues](https://mlsanity.com/paper/2402.10689/) (2024) - [Cultural Compass: Predicting Transfer Learning Success in Offensive Language Detection with Cultural Features](https://mlsanity.com/paper/2310.06458/) (2023) - [Bridging Cultural Nuances in Dialogue Agents through Cultural Value Surveys](https://mlsanity.com/paper/2401.10352/) (2024) - [Large Human Language Models: A Need and the Challenges](https://mlsanity.com/paper/2312.07751/) (2023) - [Exploring Multimodal Challenges in Toxic Chinese Detection: Taxonomy, Benchmark, and Findings](https://mlsanity.com/paper/2505.24341/) (2025) - [Lost in Pronunciation: Detecting Chinese Offensive Language Disguised by Phonetic Cloaking Replacement](https://mlsanity.com/paper/2507.07640/) (2025) - [Cultural Adaptation of Recipes](https://mlsanity.com/paper/2310.17353/) (2023) - [Chinese Offensive Language Detection:Current Status and Future Directions](https://mlsanity.com/paper/2403.18314/) (2024) - [Tailored Emotional LLM-Supporter: Enhancing Cultural Sensitivity](https://mlsanity.com/paper/2508.07902/) (2025) - [Cross-lingual Offensive Language Detection: A Systematic Review of Datasets, Transfer Approaches and Challenges](https://mlsanity.com/paper/2401.09244/) (2024) - [Data-Efficient Hate Speech Detection via Cross-Lingual Nearest Neighbor Retrieval with Limited Labeled Data](https://mlsanity.com/paper/2505.14272/) (2025) - [Culture Matters in Toxic Language Detection in Persian](https://mlsanity.com/paper/2506.03458/) (2025) - [Exploring the Capability Boundaries of LLMs in Mastering of Chinese Chouxiang Language](https://mlsanity.com/paper/2604.15841/) (2026) - [CRAFT: Cultural Russian-Oriented Dataset Adaptation for Focused Text-to-Image Generation](https://mlsanity.com/paper/2505.04851/) (2025) - [Stronger Together: Unleashing the Social Impact of Hate Speech Research](https://mlsanity.com/paper/2505.13251/) (2025) - [The Hidden Language of Harm: Examining the Role of Emojis in Harmful Online Communication and Content Moderation](https://mlsanity.com/paper/2506.00583/) (2025) ## Abstract (arXiv) Detecting offensive language is a challenging task. Generalizing across different cultures and languages becomes even more challenging: besides lexical, syntactic and semantic differences, pragmatic aspects such as cultural norms and sensitivities, which are particularly relevant in this context, vary greatly. In this paper, we target Chinese offensive language detection and aim to investigate the impact of transfer learning using offensive language detection data from different cultural backgrounds, specifically Korean and English. We find that culture-specific biases in what is considered offensive negatively impact the transferability of language models (LMs) and that LMs trained on diverse cultural data are sensitive to different features in Chinese offensive language detection. In a few-shot learning scenario, however, our study shows promising prospects for non-English offensive language detection with limited resources. Our findings highlight the importance of cross-cultural transfer learning in improving offensive language detection and promoting inclusive digital spaces.