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

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
The Use of Residual Analysis to Improve the Error Rate Accuracy of Machine Translation
Original language description
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'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.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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