This paper describes our submission toSemEval-2026 Task 9, Subtask 2 (Hausa). Thetask involves identifying specific categories ofpolarization (Political, Religious, Ethnic, etc.)in Hausa social media comments. The datasetpresented significant challenges, primarily extreme class imbalance and the low-resourcenature of the language. Our system uses a pretrained multilingual transformer (Afro-XLMRLarge) fine-tuned with Weighted Binary CrossEntropy loss and dynamic undersampling (1:3ratio) to mitigate the scarcity of polarized examples. On the official test set, our systemachieved an official Macro-F1 score of 0.2346and a Micro-F1 score of 0.2581. Our model isrecall-oriented (Micro-Recall: 0.6166), demonstrating strong capability in detecting polarization, though precision remains a challenge(0.1632). We achieved our best per-class performance in the Political domain (F1: 0.48).

This paper presents Team HausaNLP’s submission to SemEval-2026 Task 4 (Track A),which requires identifying the more narrativelysimilar of two candidate stories relative to ananchor. Narrative similarity is defined alongthree dimensions: abstract theme, course ofaction, and story outcomes. We conduct a systematic ablation comparing five approaches:a lexical TF-IDF baseline, two bi-encoderSBERT variants (all-MiniLM-L6-v2 andall-mpnet-base-v2), a paraphrase-focusedembedding model, and a cross-encoder reranker. On the 200-instance development set,all-mpnet-base-v2 achieves the best performance (61.5% accuracy, 61.48 macro-F1), outperforming both TF-IDF (54.5%) and the official SBERT baseline (55.0%). Surprisingly,the cross-encoder re-ranker (55.5%) does notimprove on the bi-encoders, which we attributeto the long-document nature of Wikipedia storysummaries exceeding the model’s effective context window. On the official test set, our primary SBERT MiniLM submission achieved61.50% accuracy (33rd of 44 teams). Our erroranalysis over 200 development instances identifies five systematic failure categories, distinctfrom the All Correct / Partial cases, including23 Lexical Trap cases, 23 Hard Cases, and 24Proposed-Recovery cases, thereby informingconcrete directions for future work.

This paper describes our participation in SemEval2026 Task 3: Dimensional Aspect-Based SentimentAnalysis (DimABSA) (Yu et al., 2026). We utilizeda pre-trained DeBERTa-V3 backbone to capturesemantic meaning through disentangled attention.While standard Mean Squared Error (MSE) loss establishes a performance floor, we propose a HybridMSE-CCCLoss to identify distributional relationships that simple regression missed. Our resultsdemonstrate a 54.6% reduction in validation losscompared to the baseline, significantly improvingdetection in high-intensity emotional bins by mitigating the "regression to the mean" phenomenon.

We describe HausaNLP’s submission toSemEval-2026 Task 7 Track 1 (short-answercultural question answering). Our system is atraining-free, prompt-based pipeline targetingnative Hausa (ha-NG). Two design decisionsdistinguish it from a generic zero-shot baseline.We use locale-conditional prompting: ha-NGquestions receive a system prompt instructingconcise standard Hausa output with explicitBoko-script characters (á, â, Î, ű). Second, weuse a two-model fallback pipeline: GPT-4o handles the primary pass, and Gemini 1.5 Flash retries any rows where the primary call returnedan error or empty output, separating modelknowledge failures from API-availability failures. On the official development leaderboard,our best run reached 36.4 accuracy. Error analysis shows that a non-trivial fraction of failures are placeholder strings caused by APIerrors rather than incorrect generations, andthat surface-level mismatches (verbosity, orthographic variation) account for many of the remaining errors. Code, prompts, and processingscripts are released for reproducibility.

Language identification (LID) is a fundamental step in curating multilingual corpora. However, LID models still perform poorly for many languages, especially on the noisy and heterogeneous web data often used to train multilingual language models. In this paper, we introduce CommonLID, a community-driven, human-annotated LID benchmark for the web domain, covering 109 languages. Many of the included languages have been previously under-served, making CommonLID a key resource for developing more representative high-quality text corpora. We show CommonLID’s value by using it, alongside five other common evaluation sets, to test eight popular LID models. We analyse our results to situate our contribution and to provide an overview of the state of the art. In particular, we highlight that existing evaluations overestimate LID accuracy for many languages in the web domain. We make CommonLID and the code used to create it available under an open, permissive license.