--- title: "Gradient Descenders at SemEval-2026 Task 9: Data-Centric" description: "The paper describes the Gradient Descenders system for SemEval-2026 Task 9 Subtask 2. 11 references." authors: ["Tran Phuoc Thanh Nhan", "Dang Van Thin"] year: 2026 venue: "Proceedings of the 20th International Workshop on Semantic Evaluation (2026)" source: "https://aclanthology.org/2026.semeval-1.113/" doi: "10.18653/v1/2026.semeval-1.113" licence: "CC BY 4.0 (ACL Anthology)" licence_url: "https://creativecommons.org/licenses/by/4.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/nhan-2026-gradient-descenders-semeval-2026-task/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/nhan-2026-gradient-descenders-semeval-2026-task/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2026](https://md.research.elamur.ai/free/en/papers/2026/page/31/) / Gradient Descenders at SemEval-2026 Task 9: Data-Centric Counterfactual Augmentation for Multi-Label Hate Speech Detection # Gradient Descenders at SemEval-2026 Task 9: Data-Centric Counterfactual Augmentation for Multi-Label Hate Speech Detection **Authors:** [Tran Phuoc Thanh Nhan](https://md.research.elamur.ai/free/en/authors/tran-phuoc-thanh-nhan/) and [Dang Van Thin](https://md.research.elamur.ai/free/en/authors/dang-van-thin/) · **Year:** 2026 · **Venue:** [Proceedings of the 20th International Workshop on Semantic Evaluation (2026)](https://md.research.elamur.ai/free/en/venues/semeval/) ([SemEval 2026](https://md.research.elamur.ai/free/en/venues/semeval/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/) **Links:** [Publisher's page](https://aclanthology.org/2026.semeval-1.113/) · [PDF](https://aclanthology.org/2026.semeval-1.113.pdf) · [DOI 10.18653/v1/2026.semeval-1.113](https://doi.org/10.18653/v1/2026.semeval-1.113) · [OpenAlex](https://openalex.org/W7166832033) **Citations:** 0 citations (OpenAlex, 2026-10-14) ## Summary The paper describes the Gradient Descenders system for SemEval-2026 Task 9 Subtask 2. The task is multi-label hate speech detection over five categories: political, racial/ethnic, religious, gender/sexual and other. The authors used only the English data, which has two problems. Political content strongly dominates, and identity-based hate almost never appears without political framing. Because of this, a classifier may treat political vocabulary as the signal for toxicity. To counter this, the team generated counterfactual training examples with an LLM using three prompting strategies: - **Additive label-flipping:** adds identity-based attacks to texts that are already toxic. - **Context decoupling:** removes political framing so that only identity hate remains. - **Cross-domain identity substitution:** swaps one targeted group for another. They then fine-tuned a Twitter-adapted RoBERTa-base model with an MLP classification head on the expanded corpus. The final system scored 44.15% Macro-F1 on the official test set, above the organizer baseline. The work shows that targeted synthetic data, rather than changes to the model architecture, can improve recall on rare classes in multi-label settings with heavy imbalance and overlapping labels. ## Key points - **Original data:** The English training set has 3,222 samples. Political labels make up over 54.95% of labels. Purely racial samples account for only 0.25%, and pure religious or gender samples are nearly absent. - **Effect of augmentation on label counts:** - Political: 1,150 → 3,615 - Racial: 281 → 1,689 - Religious: 112 → 1,125 - Gender: 72 → 1,108 - Other: 126 → 827 - The 2,047 "no label" samples were left unchanged. - **Sampling targets:** Two-label intersections were upsampled to 500 examples each. Three-label combinations and the newly created "pure" classes were capped at 300 each. - **Model and training:** The backbone is a RoBERTa-base model pretrained on 154M tweets (Loureiro et al., 2023). Preprocessing used ekphrasis. Training used BCE loss, a 0.5 decision threshold, lr 2e-5, batch size 16, 10 epochs and AdamW, on Kaggle T4 GPUs. - **Result:** The system reached a Macro-F1 of 44.15% on the official test set, outperforming the organizer baseline. *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 - [Vietnam National University Ho Chi Minh City](https://md.research.elamur.ai/free/en/universities/vietnam-national-university-ho-chi-minh-city/) — University, Ho Chi Minh City, Vietnam · 21 works · 14 citations - [University of Information Technology](https://md.research.elamur.ai/free/en/universities/university-of-information-technology/) — University, Ho Chi Minh City, Vietnam · 13 works · 7 citations 📚 Shelves (4): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 16,667 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 16,194 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 16,039 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 15,891 works 🧭 Topics (2): [😊 Sentiment & emotion](https://md.research.elamur.ai/free/en/topics/sentiment-emotion/) · 972 works · [🛡️ Bias, fairness & safety](https://md.research.elamur.ai/free/en/topics/bias-fairness-safety/) · 517 works ## Cite Cite as: Tran Phuoc Thanh Nhan and Dang Van Thin. 2026. Gradient Descenders at SemEval-2026 Task 9: Data-Centric Counterfactual Augmentation for Multi-Label Hate Speech Detection. Proceedings of the 20th International Workshop on Semantic Evaluation (2026). The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{nhan2026gradient, title = {{Gradient Descenders at SemEval-2026 Task 9: Data-Centric Counterfactual Augmentation for Multi-Label Hate Speech Detection}}, author = {Tran Phuoc Thanh Nhan and Dang {Van Thin}}, booktitle = {Proceedings of the 20th International Workshop on Semantic Evaluation (2026)}, year = {2026}, doi = {10.18653/v1/2026.semeval-1.113}, url = {https://doi.org/10.18653/v1/2026.semeval-1.113} } ``` - [← previous GradeLegal: Automated Grading for German Legal Cases](https://md.research.elamur.ai/free/en/papers/zubaer-2026-gradelegal-automated-grading-german-legal/) - [next → Graduating the Benchmark Scale: Lessons from Thermometry](https://md.research.elamur.ai/free/en/papers/trott-2026-graduating-benchmark-scale-lessons-thermometry/) - [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/31/) ## References 11 references, 4 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. 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Organizer Baselines mBERT XLM-RoBERTa RemBERT twitter-roberta-hate LaBSE 31.29 27.33 43.17 36.99 34.68 Our Experiments Internal Baseline Ours (Augmented) 29.22 44.15 67.48 52.73 64.50 57.94 Improvement (∆) +14.93 -2.98 +5.21 +1.84 +1.… [find](https://scholar.google.com/scholar?q=Micro%2DAverage+P+R+F1+Pol%2E+Class%2Dwise+F1%2DScore+Rac%2E+Rel%2E+Gen%2E+Oth%2E+Organizer+Baselines+mBERT+XLM%2DRoBERTa+RemBERT+twitter%2Droberta%2Dhate+LaBSE+31%2E29+27%2E33+43%2E17+36%2E99+34%2E68+Our+Experiments+Internal+Baseli) *Updated 1 November 2026*