This paper explores the lemmatization of multi-word expressions (MWEs) and proper names in Polish -tasks complicated by linguistic irregularities and historical factors.Instead of using rule-based methods, we apply a machine learning approach with fine-tuned plT5 and mT5 models.We trained and validated the models on enhanced gold-standard data from the 2019 Pol-Eval task and evaluated the impact of additional fine-tuning on a silver-standard dataset derived from Wikipedia.Two setups were tested: one without context, and one using leftside context of the target MWE.Our best model achieved 86.23% AccCS (Accuracy Case-Sensitive), 89.43% AccCI (Accuracy Case-Insensitive), and a combined score of 88.79%, setting a new state-of-the-art for Polish MWE and named entity lemmatization, as confirmed by the PolEval maintainers.We also evaluated optimization and quantization techniques to reduce model size and inference time with modest quality loss.

The disambiguation of abbreviations and acronyms is a longstanding problem in Natural Language Processing (NLP) that has garnered significant attention from researchers.Previous approaches have employed statistical methods, semantic similarity metrics, and machine learning algorithms.Various languages and document types have been explored, with English being the most commonly studied language.Recent advances have been driven by the application of pre-trained transformer models.Standardization and addressing the challenges of multilingual and multi-document type disambiguation remain ongoing goals in the field of NLP.This paper presents an in-depth exploration of abbreviation disambiguation using state-of-the-art neural Encoder-Decoder models, specifically the ByT5 and plT5 architectures.Advanced synthetic data generation techniques are introduced and their effect on model performance is analysed.The methods are evaluated in the context of the PolEval abbreviation disambiguation competition, where the authors achieve top ranking.