Elamur Research⌕
☰
home / papers / 2026 / Penetrating Linguistic Disguises: A Slang-aware Label-Aligned Framework for Fine-Grained Toxicity Extraction in Chinese Hate Speech Detection

Penetrating Linguistic Disguises: A Slang-aware Label-Aligned Framework for Fine-Grained Toxicity Extraction in Chinese Hate Speech Detection

Paper · 2026 · Wei Liu, Xiaoliang Chen, Duoqian Miao et al.

Summary

The paper addresses span-level hate speech extraction in Chinese, where unclear word boundaries and deliberate obfuscation (slang, homophones, pinyin acronyms) make exact matching hard. On the STATE ToxiCN benchmark, systems must output Target-Argument-Hateful-Group quadruples. Generative LLMs tend to shift span boundaries. Standard 2D grid taggers break down when a single span pair carries several hate categories at once. The authors propose SLAF (Slang-aware Label-Aligned Framework), a discriminative model with two components on top of a PLM encoder. The first is SSLF, which brings in a slang lexicon via structure-aware attention, so that disguised terms are linked to their hateful meaning. The second is LDVT, which extends grid tagging to an L × L × N volume with separate channels per label, so overlapping categories can be decoded without heuristic post-processing. SLAF reaches a Hard-F1 of 30.09% on STATE ToxiCN. This is 5.82 points above the strongest fine-tuned LLM baseline. The result suggests that boundary-aware structured models remain valuable for exact-match toxicity extraction.

Key points

  • Task and labels: The task is quadruple extraction on STATE ToxiCN.
    • Category labels are Sexism, Racism, Region, LGBTQ, Others and Non-hate, predicted as a multi-label task.
    • The grid labels follow Li et al. (2023): tgt/arg for entity boundaries and h2h/t2t for relations.
  • SSLF: This module is built from a pair of components, Semantic Category Aggregation and Structural Prior Injection. Lexicon cues are conditioned on context rather than simply concatenated as static features.
  • Baselines: Comparisons include fine-tuned mT5-base, Mistral-7B and LLaMA3-8B, as well as prompted GPT-4o and DeepSeek-v3.
  • Results under label collision: Performance was measured by how many categories a span pair carries.
    • 2D Grid + Label Combo drops to 0% F1 at 2 categories.
    • 2D Grid + Sigmoid drops to 0% at ≥3 categories.
    • SLAF retains about 12% F1 at ≥3 categories.
  • Limitations and availability:
    • The model depends on lexicon coverage, so new slang can be missed.
    • LDVT uses more memory, scaling as O(N × d).
    • Only one Chinese quadruple dataset currently exists for evaluation.
    • Code is available on GitHub (HatsuneMikuLW/SLAF). The data is under CC BY-NC 4.0 and is not redistributed.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🏛️Chinese Academy of SciencesBeijing · China194 works · 23,906 citations🎓Tongji UniversityShanghai · China19 works · 2,162 citations

Also on the byline: Xihua University.

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

🧭 Topics 2Research subjects and languages the work is about.Sentiment & emotion · 972 worksBias, fairness & safety · 517 works

Cite

Cite as Wei Liu, Xiaoliang Chen, Duoqian Miao, Xu Gu, Xianyong Li and Yajun Du. 2026. Penetrating Linguistic Disguises: A Slang-aware Label-Aligned Framework for Fine-Grained Toxicity Extraction in Chinese Hate Speech Detection. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.findings-acl.1061

@inproceedings{liu2026penetrating,
  title = {{Penetrating Linguistic Disguises: A Slang-aware Label-Aligned Framework for Fine-Grained Toxicity Extraction in Chinese Hate Speech Detection}},
  author = {Wei Liu, Xiaoliang Chen, Duoqian Miao, Xu Gu, Xianyong Li and Yajun Du},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.1061},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.1061}
}

References

34 references, 10 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Hyeseon Ahn, Youngwook Kim, Jungin Kim, and Yo-Sub Han. 2024. Sharedcon: Implicit hate speech detection using shared semantics. In Findings of the Association for Computational Linguistics, ACL 2024, Bangkok, Thailand and virtual meeting, August 11-16, 2024, pages 10444–10455. Association for Comput… find
  2. Raza Ali, Umar Farooq, Muhammad Umair Arshad, Waseem Shahzad, and Mirza Omer Beg. 2022. Hate speech detection on twitter using transfer learning. Comput. Speech Lang., 74:101365. find
  3. Badr AlKhamissi, Faisal Ladhak, Srini Iyer, Veselin Stoyanov, Zornitsa Kozareva, Xian Li, Pascale Fung, Lambert Mathias, Asli Celikyilmaz, and Mona T. Diab. 2022. Token: Task decomposition and knowledge infusion for few-shot hate speech detection. In Proceedings of the 2022 Conference on Empirical M… find
  4. Anthropic. 2024. Claude 3.5 sonnet model card addendum. Technical report, Anthropic. Model card addendum (official report). find
  5. Leandro Araújo, Mainack Mondal, Denzil Correa, Fabrício Benevenuto, and Ingmar Weber. 2016. Analyzing the targets of hate in online social media. In Proceedings of the Tenth International Conference on Web and Social Media, Cologne, Germany, May 17-20, 2016, pages 687–690. AAAI Press. find
  6. Zewen Bai, Liang Yang, Shengdi Yin, Junyu Lu, Jingjie Zeng, Haohao Zhu, Yuanyuan Sun, and Hongfei Lin. 2025. STATE ToxiCN: A benchmark for span-level target-aware toxicity extraction in Chinese hate speech detection. In Findings of the Association for Computational Linguistics: ACL 2025, pages 10206… find
  7. Michał Bilewicz and Wiktor Soral. 2020. Hate speech epidemic. the dynamic effects of derogatory language on intergroup relations and political radicalization. Political Psychology. find
  8. Tommaso Caselli, Valerio Basile, Jelena Mitrovic, and Michael Granitzer. 2020. Hatebert: Retraining BERT for abusive language detection in english. CoRR, abs/2010.12472.
  9. Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Shijin Wang, and Guoping Hu. 2020. Revisiting pre-trained models for chinese natural language processing. In Findings of the Association for Computational Linguistics: EMNLP 2020, Online Event, 16-20 November 2020, volume EMNLP 2020 of Findings of ACL, p…
  10. Thomas Davidson, Dana Warmsley, Michael W. Macy, and Ingmar Weber. 2017. Automated hate speech detection and the problem of offensive language. In Proceedings of the Eleventh International Conference on Web and Social Media, ICWSM 2017, Montréal, Québec, Canada, May 15-18, 2017, pages 512–515. AAAI…
  11. DeepSeek-AI. 2024. Deepseek-v3 technical report. CoRR, abs/2412.19437. find
  12. Jiawen Deng, Jingyan Zhou, Hao Sun, Chujie Zheng, Fei Mi, Helen Meng, and Minlie Huang. 2022. COLD: A benchmark for chinese offensive language detection. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022, Abu Dhabi, United Arab Emirates, December 7… find
  13. Paula Fortuna and Sérgio Nunes. 2018. A survey on automatic detection of hate speech in text. ACM Comput. Surv., 51(4):85:1–85:30. find
  14. Gemini Team. 2024. Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context. Technical report, Google. Technical report (official PDF).
  15. Ridong Han, Tao Peng, Chaohao Yang, Benyou Wang, Lu Liu, and Xiang Wan. 2023. Is information extraction solved by chatgpt? an analysis of performance, evaluation criteria, robustness and errors. CoRR, abs/2305.14450. — [arXiv:2305.14450](https://arxiv.org/abs/2305.14450) find
  16. Laura Hanu and Unitary team. 2020. Detoxify. Github. https://github.com/unitaryai/detoxify.15th International Workshop on Semantic Evaluation (SemEval-2021), pages 59–69, Online. Association for Computational Linguistics. ↗
  17. Aaron Hurst, Adam Lerer, Adam P. Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostrow, Akila Welihinda, Alan Hayes, Alec Radford, Aleksander Madry, Alex Baker-Whitcomb, Alex Beutel, Alex Borzunov, Alex Carney, Alex Chow, Alex Kirillov, Alex Nichol, Alex Paino, and 79 others. 2024. Gpt-4o sy…
  18. Jianlin Su, Murtadha H. M. Ahmed, Yu Lu, Shengfeng Pan, Wen Bo, and Yunfeng Liu. 2024. Roformer: Enhanced transformer with rotary position embedding. Neurocomputing, 568:127063.
  19. Aiqi Jiang, Xiaohan Yang, Yang Liu, and Arkaitz Zubiaga. 2022. Swsr: A chinese dataset and lexicon for online sexism detection. Online Social Networks and Media, 27:100182. find
  20. Qwen Team. 2024b. Qwen2.5 technical report. CoRR, abs/2412.15115. find
  21. Albert Q. Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Elena Hanna, Simon Bressand, and 1 others. 2023. Mistral 7b. CoRR, abs/2310.06825. find
  22. Bobo Li, Hao Fei, Fei Li, Yuhan Wu, Jinsong Zhang, Shengqiong Wu, Jingye Li, Yijiang Liu, Lizi Liao, Tat-Seng Chua, and Donghong Ji. 2023. DiaASQ: A benchmark of conversational aspect-based sentiment quadruple analysis. In Findings of the Association for Computational Linguistics: ACL 2023, pages 13… find
  23. Junyu Lu, Bo Xu, Xiaokun Zhang, Changrong Min, Liang Yang, and Hongfei Lin. 2023. Facilitating fine-grained detection of chinese toxic language: Hierarchical taxonomy, resources, and benchmarks. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Lon… find
  24. Binny Mathew, Punyajoy Saha, Seid Muhie Yimam, Chris Biemann, Pawan Goyal, and Animesh Mukherjee. 2021. Hatexplain: A benchmark dataset for explainable hate speech detection. In Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of… find
  25. Endang Wahyu Pamungkas, Dian Purworini, Diah Priyawati, and Rona Rizkhy Bunga Chasana. 2023. Exploring the impact of lexicon-based knowledge transfer for hate speech detection in indonesia code-mixed languages. In Proceedings of the 2023 7th International Conference on Natural Language Processing an… find
  26. John Pavlopoulos, Jeffrey Sorensen, Léo Laugier, and Ion Androutsopoulos. 2021. SemEval-2021 task 5: Toxic spans detection. In Proceedings of the Llama Team. 2024a. The llama 3 herd of models. CoRR, abs/2407.21783.
  27. Xiao Wang, Weikang Zhou, Can Zu, Han Xia, Tianze Chen, Yuansen Zhang, Rui Zheng, Junjie Ye, Qi Zhang, Tao Gui, Jihua Kang, Jingsheng Yang, Siyuan Li, and Chunsai Du. 2023. Instructuie: Multi-task instruction tuning for unified information extraction. CoRR, abs/2304.08085.
  28. Yucheng Wang, Bowen Yu, Yueyang Zhang, Tingwen Liu, Hongsong Zhu, and Limin Sun. 2020. Tplinker: Single-stage joint extraction of entities and relations through token pair linking. In Proceedings of the 28th International Conference on Computational Linguistics, COLING 2020, Barcelona, Spain (Online…
  29. Zhen Wu, Chengcan Ying, Fei Zhao, Zhifang Fan, Xinyu Dai, and Rui Xia. 2020. Grid tagging scheme for aspect-oriented fine-grained opinion extraction. In Findings of the Association for Computational Linguistics: EMNLP 2020, Online, November 16-20, 2020, pages 2576–2585, Online. Association for Compu… find
  30. Yunze Xiao, Yujia Hu, Kenny Tsu Wei Choo, and Roy Ka-Wei Lee. 2024. ToxiCloakCN: Evaluating robustness of offensive language detection in Chinese with cloaking perturbations. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 6012–6025, Miami, Florida, U… find
  31. Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, and Colin Raffel. 2021. mT5: A massively multilingual pre-trained text-to-text transformer. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Ling…
  32. Marcos Zampieri, Skye Morgan, Kai North, Tharindu Ranasinghe, Austin Simmmons, Paridhi Khandelwal, Sara Rosenthal, and Preslav Nakov. 2023. Target-based offensive language identification. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Pape… find
  33. Wenjun Zeng, Yuchi Liu, Ryan Mullins, Ludovic Peran, Joe Fernandez, Hamza Harkous, Karthik Narasimhan, Drew Proud, Piyush Kumar, Bhaktipriya Radharapu, Olivia Sturman, and Oscar Wahltinez. 2024. Shieldgemma: Generative AI content moderation based on gemma. CoRR, abs/2407.21772. — [arXiv:2407.21772](https://arxiv.org/abs/2407.21772) find
  34. Zhexin Zhang, Yida Lu, Jingyuan Ma, Di Zhang, Rui Li, Pei Ke, Hao Sun, Lei Sha, Zhifang Sui, Hongning Wang, and Minlie Huang. 2024. ShieldLM: Empowering LLMs as aligned, customizable and explainable safety detectors. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 104… find

Updated

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/papers/liu-2026-penetrating-linguistic-disguises-slang-aware/