Research efforts aimed at detecting unsafe dialogues have resulted in creation of benchmark datasets and models for evaluation. The benchmarks mostly exist in English and other high resourced languages. In order to address the challenge of unavailability of dialogue safety evaluation dataset in Hausa and Yorùbá, we repurporse DiaSafety dataset to develop HaYo dataset, by providing contextualised human annotation of dialogues in DiaSafety. We provide dialogues in Hausa and Yorùbá, obtained by human translation of dialogues in the DiaSafety dataset, to raters who are native speakers. The dialogues are annotated as Unsafe or Safe. We evaluate seven models with moderation, conversational or multilingual capabilities in terms of F1 Score. Using McNemar test, we observe that the predictions of GPT-4.1 and Gemma-3-12b-it on HaYo are statistically significant at p < 0.05. In our evaluation with instructions in English, we observe lower F1 scores in six out of the seven models, comparing the performance on DiaSafety and HaYo labels. The model predictions were inconsistent with the labels in the HaYo dataset when instructions and dialogues were provided in Hausa and Yorùbá. Compared to providing instructions in English, the issues range from responses in unspecified languages to underperformance in terms of F1 score. We plan to release the HaYo dataset to the public to promote dialogue safety research, especially in under-resourced languages.

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.