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.

People worldwide use language in subtle and complex ways to express emotions. Although emotion recognition–an umbrella term for several NLP tasks–impacts various applications within NLP and beyond, most work in this area has focused on high-resource languages. This has led to significant disparities in research efforts and proposed solutions, particularly for under-resourced languages, which often lack high-quality annotated datasets.In this paper, we present BRIGHTER–a collection of multi-labeled, emotion-annotated datasets in 28 different languages and across several domains. BRIGHTER primarily covers low-resource languages from Africa, Asia, Eastern Europe, and Latin America, with instances labeled by fluent speakers. We highlight the challenges related to the data collection and annotation processes, and then report experimental results for monolingual and crosslingual multi-label emotion identification, as well as emotion intensity recognition. We analyse the variability in performance across languages and text domains, both with and without the use of LLMs, and show that the BRIGHTER datasets represent a meaningful step towards addressing the gap in text-based emotion recognition.