Language Technology Platform for Public Administration

Skadiņš, Raivis; Pinnis, Mārcis; Vasiļevskis, Artūrs; Vasiļjevs, Andrejs; Sics, Valters; Rozis, Roberts; Lagzdiņš, Andis

doi:10.3233/faia200621

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…Our findings are in line with the other studies 58 – 63 that focused on comparing SMT and NMT quality across various text genres. Benkova et al 63 conducted similar research, using residual analysis and the automatic metric BLEU-n to compare quality between neural and statistical MT systems.…”

Section: Discussionsupporting

confidence: 92%

The use of residual analysis to improve the error rate accuracy of machine translation

Benko,

Munkova,

Munk

et al. 2024

Sci Rep

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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.

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Section: Discussionsupporting

confidence: 92%

The use of residual analysis to improve the error rate accuracy of machine translation

Benko,

Munkova,

Munk

et al. 2024

Sci Rep

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“…Nowadays, there are countless online MT sites for general use that offer access to MT either from companies that make the systems freely available with some usage restrictions (Amazon, Google, Microsoft, DeepL, and Tilde among others) or from public bodies that facilitate their custom-based MT capabilities (the European Commission and the Basque and Latvian governments, among others, Skadins et al 2020). People use these tools to translate a very diverse range of texts.…”

Section: State-of-the-artmentioning

confidence: 99%

Deep Dive Machine Translation

Skadiņa

Vasiļjevs

Pinnis

et al. 2023

Cognitive Technologies

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Machine Translation (MT) is one of the oldest language technologies having been researched for more than 70 years. However, it is only during the last decade that it has been widely accepted by the general public, to the point where in many cases it has become an indispensable tool for the global community, supporting communication between nations and lowering language barriers. Still, there remain major gaps in the technology that need addressing before it can be successfully a0146pplied in under-resourced settings, can understand context and use world knowledge. This chapter provides an overview of the current state-of-the-art in the field of MT, offers technical and scientific forecasting for 2030, and provides recommendations for the advancement of MT as a critical technology if the goal of digital language equality in Europe is to be achieved.

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Language Technology Platform for Public Administration

Cited by 2 publications

References 6 publications

The use of residual analysis to improve the error rate accuracy of machine translation

The use of residual analysis to improve the error rate accuracy of machine translation

Deep Dive Machine Translation

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