This paper presents the first machine translation system for Shughni, an extremely lowresource Eastern Iranian language spoken in Tajikistan and Afghanistan. We fine-tune NLLB-200 models and explore auxiliary language selection through typological similarity and "super-donor" experiments. Our final Shughni–Russian model achieves a chrF++ score of 36.3 (45.7 on BivalTyp data), establishing the first computational translation resource for this language. Beyond reporting system performance, this work demonstrates a practical path toward supporting languages with virtually no prior MT resources. Our demo system with Shughni-Russian- English translation (Russian serves as a pivot language for the Shughni- English pair) is available on Hugging- Face (https://huggingface.co/spaces/Novokshanov/Shughni-Translator).

Active learning (AL) has demonstrated remarkable potential in reducing the annotation effort required for training machine learning models. However, despite the surging popularity of natural language generation (NLG) tasks in recent years, the application of AL to NLG has been limited. In this paper, we introduce Active Text Generation (ATGen) - a comprehensive framework that bridges AL with text generation tasks, enabling the application of state-of-the-art AL strategies to NLG. Our framework simplifies AL-empowered annotation in NLG tasks using both human annotators and automatic annotation agents based on large language models (LLMs). The framework supports LLMs deployed as a service, such as ChatGPT and Claude, or operated on-premises. Furthermore, ATGen provides a unified platform for smooth implementation and benchmarking of novel AL strategies tailored to NLG tasks. Finally, we present experimental results across multiple text generation tasks where we compare the performance of state-of-the-art AL strategies in various settings. We demonstrate that ATGen can reduce both the effort of human annotators and costs for API calls to automatic annotation agents based on LLMs.