We present EpiGator, a novel event-based system for global surveillance of outbreaks of infectious epidemics that automatically processes streams of news articles and generates reports about the outbreaks, which is crucial for medical authorities. The goal of our work is to combine our experience in outbreak surveillance with state-of-the-art large language models (LLM), which allows us to reduce the overall cost of system development and maintenance. The EpiGator pipeline combines keyword filtering, relevance classification, event-based clustering, and multi-document summarization. A key novelty lies in using a fine-tuned LLM to identify articles relevant to ongoing outbreaks, followed by a zero-shot information extraction pipeline that normalizes the event features and clusters the related articles. For each cluster, we generate an outbreak summary using instruction-tuned LLMs. We evaluate EpiGator output against disease outbreak reports written by medical specialists.

We observe that fine-tuned BERT outperforms feature-based models at estimation of text difficulty, but it is unclear what drives this advantage. We explore a state-of-the-art Finnish BERT difficulty estimator and analyze it against a strong feature-based model trained on 66 hand-crafted linguistic features. First, we find that BERT implicitly encodes the coarse length and complexity features, but not fine-grained morphology of Finnish. Second, BERT subsumes the information in the hand-crafted features and adds signal that they miss: the two models are evenly matched on most documents, and BERT’s advantage comes from rarely being very wrong, rather than from winning everywhere. Third, through input perturbation and layer-wise probing, we show that BERT’s predictions rely on sentence-internal structure rather than on lexical content alone. Our analysis explains why a neural difficulty estimator succeeds and what linguistic information it depends on.

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.