Abbreviation Disambiguation in Polish Press News Using Encoder-Decoder Models

Abstract

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.