Tailored Fine-tuning for Comma Insertion in Czech

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F26%3A3Q8YSIL2" target="_blank" >RIV/00216208:11320/26:3Q8YSIL2 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:90244/25:10513784

Výsledek na webu

<a href="http://dx.doi.org/10.2478/jazcas-2025-0024" target="_blank" >http://dx.doi.org/10.2478/jazcas-2025-0024</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.2478/jazcas-2025-0024" target="_blank" >10.2478/jazcas-2025-0024</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Tailored Fine-tuning for Comma Insertion in Czech

Popis výsledku v původním jazyce

Transfer learning techniques, particularly the use of pre-trained Transformers, can be trained on vast amounts of text in a particular language and can be tailored to specific grammar correction tasks, such as automatic punctuation correction. The Czech pre-trained RoBERTa model demonstrates outstanding performance in this task (Machura et al. 2022); however, previous attempts to improve the model have so far led to a slight degradation (Machura et al. 2023). In this paper, we present a more targeted fine-tuning of this model, addressing linguistic phenomena that the base model overlooked. Additionally, we provide a comparison with other models trained on a more diverse dataset beyond just web texts. © 2025 Jakub Machura et al., published by Slovak Academy of Sciences, Ľudovít Štúr Institute of Linguistics.

Název v anglickém jazyce

Tailored Fine-tuning for Comma Insertion in Czech

Popis výsledku anglicky

Transfer learning techniques, particularly the use of pre-trained Transformers, can be trained on vast amounts of text in a particular language and can be tailored to specific grammar correction tasks, such as automatic punctuation correction. The Czech pre-trained RoBERTa model demonstrates outstanding performance in this task (Machura et al. 2022); however, previous attempts to improve the model have so far led to a slight degradation (Machura et al. 2023). In this paper, we present a more targeted fine-tuning of this model, addressing linguistic phenomena that the base model overlooked. Additionally, we provide a comparison with other models trained on a more diverse dataset beyond just web texts. © 2025 Jakub Machura et al., published by Slovak Academy of Sciences, Ľudovít Štúr Institute of Linguistics.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

—

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Jazykovedny Casopis

ISSN

0021-5597

e-ISSN

—

Svazek periodika

76

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

11

Strana od-do

268-278

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-105025792668