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@proceedings{2330265,
author = {Machura, Jakub and Žižková, Hana and Švec, Jan and Frémund, Adam},
booktitle = {SLOVKO, 18 - 20 October 2023, Bratislava},
doi = {http://dx.doi.org/10.2478/jazcas-2023-0052},
keywords = {comma; Czech; vocative; machine learning; RoBERTa},
language = {eng},
title = {IS IT POSSIBLE TO RE-EDUCATE ROBERTA? EXPERT-DRIVEN MACHINE LEARNING FOR PUNCTUATION CORRECTION},
year = {2023}
}
TY - CONF
ID - 2330265
AU - Machura, Jakub - Žižková, Hana - Švec, Jan - Frémund, Adam
PY - 2023
TI - IS IT POSSIBLE TO RE-EDUCATE ROBERTA? EXPERT-DRIVEN MACHINE LEARNING FOR PUNCTUATION CORRECTION
KW - comma
KW - Czech
KW - vocative
KW - machine learning
KW - RoBERTa
N2 - Although Czech rule-based tools for automatic punctuation insertion rely on extensive grammar and achieve respectable precision, the pre-trained Transformers outperform rule-based systems in precision and recall (Machura et al. 2022). The Czech pre-trained RoBERTa model achieves excellent results, yet a certain level of phenomena is ignored, and the model partially makes errors. This paper aims to investigate whether it is possible to retrain the RoBERTa language model to increase the number of sentence commas the model correctly detects. We have chosen a very specific and narrow type of sentence comma, namely the sentence comma delimiting vocative phrases, which is clearly defined in the grammar and is very often omitted by writers. The chosen approaches were further tested and evaluated on different types of texts
ER -
MACHURA, Jakub, Hana ŽIŽKOVÁ, Jan ŠVEC a Adam FRÉMUND. IS IT POSSIBLE TO RE-EDUCATE ROBERTA? EXPERT-DRIVEN MACHINE LEARNING FOR PUNCTUATION CORRECTION. In \textit{SLOVKO, 18 - 20 October 2023, Bratislava}. 2023. Dostupné z: https://dx.doi.org/10.2478/jazcas-2023-0052.
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