We introduce UAReviews, a multi-task Ukrainian-language dataset for emotion and intent classification comprising 11,580 annotated texts. The dataset combines two sources: citizen reviews of government digital services provided by the Ministry of Digital Transformation of Ukraine and Ukrainian-language Telegram posts drawn from the COSMUS corpus. Each text is annotated with both an emotion label following the Ekman taxonomy (seven classes) and an intent label (five classes), making it the first publicly available Ukrainian resource for joint emotion and intent analysis. Annotation was performed by students at the Anonymous Institution, with a gold standard subset (20\%) validated by three independent annotators achieving Krippendorff’s alpha = 0.93. We establish baselines using single-task and multi-task fine-tuned XLM-RoBERTa models and analyze emotion to intent correlation. Both the dataset and the baseline models are publicly available.

In this paper we introduce the first effort to adapt large language models (LLMs) to the Ukrainian dialect (in our case Hutsul), a low-resource and morphologically complex dialect spoken in the Carpathian Highlands. We created a parallel corpus of 9852 dialect-to-standard Ukrainian sentence pairs and a dictionary of 7320 dialectal word mappings. We also addressed data shortage by proposing an advanced Retrieval-Augmented Generation (RAG) pipeline to generate synthetic parallel translation pairs, expanding the corpus with 52142 examples. We have fine-tuned multiple open-source LLMs using LoRA and evaluated them on a standard-to-dialect translation task, also comparing with few-shot GPT-4o translation. In the absence of human annotators, we adopt a multi-metric evaluation strategy combining BLEU, chrF++, TER, and LLM-based judgment (GPT-4o). The results show that even small(7B) finetuned models outperform zero-shot baselines such as GPT-4o across both automatic and LLM-evaluated metrics. All data, models, and code are publicly released at: https://github.com/woters/vuyko-hutsul.