Volodymyr Mudryi


2023

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Contextual Embeddings for Ukrainian: A Large Language Model Approach to Word Sense Disambiguation
Yurii Laba | Volodymyr Mudryi | Dmytro Chaplynskyi | Mariana Romanyshyn | Oles Dobosevych
Proceedings of the Second Ukrainian Natural Language Processing Workshop (UNLP)

This research proposes a novel approach to the Word Sense Disambiguation (WSD) task in the Ukrainian language based on supervised fine-tuning of a pre-trained Large Language Model (LLM) on the dataset generated in an unsupervised way to obtain better contextual embeddings for words with multiple senses. The paper presents a method for generating a new dataset for WSD evaluation in the Ukrainian language based on the SUM dictionary. We developed a comprehensive framework that facilitates the generation of WSD evaluation datasets, enables the use of different prediction strategies, LLMs, and pooling strategies, and generates multiple performance reports. Our approach shows 77,9% accuracy for lexical meaning prediction for homonyms.