The Use of Residual Analysis to Improve the Error Rate Accuracy of Machine Translation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F24%3A39922246" target="_blank" >RIV/00216275:25410/24:39922246 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.nature.com/articles/s41598-024-59524-3" target="_blank" >https://www.nature.com/articles/s41598-024-59524-3</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1038/s41598-024-59524-3" target="_blank" >10.1038/s41598-024-59524-3</a>

Jazyk výsledku

angličtina

Název v původním jazyce

The Use of Residual Analysis to Improve the Error Rate Accuracy of Machine Translation

Popis výsledku v původním jazyce

The aim of the study is to compare two different approaches to machine translation-statistical and neural-using automatic MT metrics of error rate and residuals. We examined four available online MT systems (statistical Google Translate, neural Google Translate, and two European commission&apos;s MT tools-statistical mt@ec and neural eTranslation) through their products (MT outputs). We propose using residual analysis to improve the accuracy of machine translation error rate. Residuals represent a new approach to comparing the quality of statistical and neural MT outputs. The study provides new insights into evaluating machine translation quality from English and German into Slovak through automatic error rate metrics. In the category of prediction and syntactic-semantic correlativeness, statistical MT showed a significantly higher error rate than neural MT. Conversely, in the category of lexical semantics, neural MT showed a significantly higher error rate than statistical MT. The results indicate that relying solely on the reference when determining MT quality is insufficient. However, when combined with residuals, it offers a more objective view of MT quality and facilitates the comparison of statistical MT and neural MT.

Název v anglickém jazyce

The Use of Residual Analysis to Improve the Error Rate Accuracy of Machine Translation

Popis výsledku anglicky

The aim of the study is to compare two different approaches to machine translation-statistical and neural-using automatic MT metrics of error rate and residuals. We examined four available online MT systems (statistical Google Translate, neural Google Translate, and two European commission&apos;s MT tools-statistical mt@ec and neural eTranslation) through their products (MT outputs). We propose using residual analysis to improve the accuracy of machine translation error rate. Residuals represent a new approach to comparing the quality of statistical and neural MT outputs. The study provides new insights into evaluating machine translation quality from English and German into Slovak through automatic error rate metrics. In the category of prediction and syntactic-semantic correlativeness, statistical MT showed a significantly higher error rate than neural MT. Conversely, in the category of lexical semantics, neural MT showed a significantly higher error rate than statistical MT. The results indicate that relying solely on the reference when determining MT quality is insufficient. However, when combined with residuals, it offers a more objective view of MT quality and facilitates the comparison of statistical MT and neural MT.

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

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Scientific Reports

ISSN

2045-2322

e-ISSN

2045-2322

Svazek periodika

14

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

19

Strana od-do

9293

Kód UT WoS článku

001207399200105

EID výsledku v databázi Scopus

2-s2.0-85191073927