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.

Semantic Text Relatedness (STR), a measure of meaning similarity between text elements, has become a key focus in the field of Natural Language Processing (NLP). We describe SemEval-2024 task 1 on Semantic Textual Relatedness featuring three tracks: supervised learning, unsupervised learning and cross-lingual learning across African and Asian languages including Afrikaans, Algerian Arabic, Amharic, Hausa, Hindi, Indonesian, Kinyarwanda, Marathi, Moroccan Arabic, Modern Standard Arabic, Punjabi, Spanish, and Telugu. Our goal is to analyse the semantic representation of sentences textual relatedness trained on mBert, all-MiniLM-L6-v2 and Bert-Based-uncased. The effectiveness of these models is evaluated using the Spearman Correlation metric, which assesses the strength of the relationship between paired data. The finding reveals the viability of transformer models in multilingual STR tasks.

We present the findings of SemEval-2023 Task 12, a shared task on sentiment analysis for low-resource African languages using Twitter dataset. The task featured three subtasks; subtask A is monolingual sentiment classification with 12 tracks which are all monolingual languages, subtask B is multilingual sentiment classification using the tracks in subtask A and subtask C is a zero-shot sentiment classification. We present the results and findings of subtask A, subtask B and subtask C. We also release the code on github. Our goal is to leverage low-resource tweet data using pre-trained Afro-xlmr-large, AfriBERTa-Large, Bert-base-arabic-camelbert-da-sentiment (Arabic-camelbert), Multilingual-BERT (mBERT) and BERT models for sentiment analysis of 14 African languages. The datasets for these subtasks consists of a gold standard multi-class labeled Twitter datasets from these languages. Our results demonstrate that Afro-xlmr-large model performed better compared to the other models in most of the languages datasets. Similarly, Nigerian languages: Hausa, Igbo, and Yoruba achieved better performance compared to other languages and this can be attributed to the higher volume of data present in the languages.