Grammatical error correction (GEC) is a natural language processing task critical for improving language quality, supporting communication efficacy, and for language learning and teaching. To date, most research in GEC has focused on major, resource-rich languages such as English, while lower-resource languages remain underexplored. In this paper, we focus on GEC for Finnish. We build a dataset based on data from real-world language learners. We explore various approaches to GEC, including fine-tuning transformer models and zero-shot LLM prompting. We also adapt ERRANT, a popular GEC evaluation tool, for the Finnish language, to evaluate the performance of the models. Our results indicate that the performance of GEC for Finnish is promising, but requires further research. To the best of our knowledge, this is the first in-depth exploration of GEC for Finnish; we provide benchmarks, datasets, and code for GEC for Finnish—by releasing our training and test data and the code for Finnish ERRANT—to support further research on this important task.

This paper presents our work on tools to support the Tatar language, using Revita, a web-based Intelligent Tutoring System for language teaching and learning. The system allows the users — teachers and learners — to upload arbitrary authentic texts, and automatically creates exercises based on these texts that engage the learners in active production of language. It provides graduated feedback when they make mistakes, and performs continuous assessment, based on which the system selects exercises for the learners at the appropriate level. The assessment also helps the students maintain their learning pace, and helps the teachers to monitor their progress.The paper describes the functionality currently implemented for Tatar, which enables learners — who possess basic proficiency beyond the beginner level — to improve their competency, using texts of their choice as learning content. Support for Tatar is being developed to increase public interest in learning the language of this important regional minority, as well as to to provide tools for improving fluency to “heritage speakers” — those who have substantial passive competency, but lack active fluency and need support for regular practice.