Papers by Karol Grzegorczyk

2 papers
Disambiguated skip-gram model (D18-1)

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Challenge: Disambiguated skip-gram is a neural-probabilistic model for learning multi-sense word embeddings.
Approach: They propose a model that is end-to-end differentiable and can be interpreted as a feed-forward neural network.
Outcome: The proposed model improves state-of-the-art in word sense induction benchmarks.
Evaluation of Transfer Learning for Polish with a Text-to-Text Model (2022.lrec-1)

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Challenge: Recent years have brought significant progress in natural language understanding (NLU) and natural language generation (NLG).
Approach: They propose a benchmark for assessing the quality of text-to-text models for Polish . they evaluate the performance of plT5, mT5, Polish BART, and Polish GPT-2 .
Outcome: The proposed model can be fine-tuned on various NLP tasks with a single training objective.

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