Parallel texts dataset for Uzbek-Kazakh machine translation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3AVMWYREKG" target="_blank" >RIV/00216208:11320/25:VMWYREKG - isvavai.cz</a>

Výsledek na webu

<a href="https://www.webofscience.com/wos/woscc/summary/121e71ce-d59a-4953-8092-7d6304231303-fed0d941/relevance/1" target="_blank" >https://www.webofscience.com/wos/woscc/summary/121e71ce-d59a-4953-8092-7d6304231303-fed0d941/relevance/1</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.dib.2024.110194" target="_blank" >10.1016/j.dib.2024.110194</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Parallel texts dataset for Uzbek-Kazakh machine translation

Popis výsledku v původním jazyce

This paper presents a parallel corpus of raw texts between the Uzbek and Kazakh languages as a dataset for machine translation applications, focusing on the data collection process, dataset description, and its potential for reuse. The dataset-building process includes three separate stages, starting with a tiny portion of already available parallel data, then some more compiled from openly available resources like literature books, and web news texts, which were aligned using the sentence alignment method, encompassing a wide range of topics and genres. Finally, the majority of the dataset was taken from a raw text corpus in Uzbek and manually translated into Kazakh by a group of experts who are fluent in both languages. The resulting parallel corpus serves as a valuable resource for researchers and practitioners interested in Kazakh and Uzbek language processing tasks, particularly in the context of neural machine translation, where the presented data can be used for testing the rule-based machine translation models, or it can be used for both training statistical and neural machine translation models as well. The dataset has been made accessible through the widely recognized Hugging Face platform, a repository known for facilitating collaborative efforts and advancing Natural Language

Název v anglickém jazyce

Parallel texts dataset for Uzbek-Kazakh machine translation

Popis výsledku anglicky

This paper presents a parallel corpus of raw texts between the Uzbek and Kazakh languages as a dataset for machine translation applications, focusing on the data collection process, dataset description, and its potential for reuse. The dataset-building process includes three separate stages, starting with a tiny portion of already available parallel data, then some more compiled from openly available resources like literature books, and web news texts, which were aligned using the sentence alignment method, encompassing a wide range of topics and genres. Finally, the majority of the dataset was taken from a raw text corpus in Uzbek and manually translated into Kazakh by a group of experts who are fluent in both languages. The resulting parallel corpus serves as a valuable resource for researchers and practitioners interested in Kazakh and Uzbek language processing tasks, particularly in the context of neural machine translation, where the presented data can be used for testing the rule-based machine translation models, or it can be used for both training statistical and neural machine translation models as well. The dataset has been made accessible through the widely recognized Hugging Face platform, a repository known for facilitating collaborative efforts and advancing Natural Language

Druh

J_imp - Článek v periodiku v databázi Web of Science

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

2024

Kód důvěrnosti údajů

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

Název periodika

DATA IN BRIEF

ISSN

2352-3409

e-ISSN

—

Svazek periodika

53

Číslo periodika v rámci svazku

2024-04

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

110194

Strana od-do

1-110194

Kód UT WoS článku

001199307700001

EID výsledku v databázi Scopus

—