Transforming machine translation: a deep learning system reaches news translation quality comparable to human professionals

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F20%3A10424334" target="_blank" >RIV/00216208:11320/20:10424334 - isvavai.cz</a>

Výsledek na webu

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=0k1mY-gfTl" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=0k1mY-gfTl</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1038/s41467-020-18073-9" target="_blank" >10.1038/s41467-020-18073-9</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Transforming machine translation: a deep learning system reaches news translation quality comparable to human professionals

Popis výsledku v původním jazyce

The quality of human translation was long thought to be unattainable for computer translation systems. In this study, we present a deep-learning system, CUBBITT, which challenges this view. In a context-aware blind evaluation by human judges, CUBBITT significantly outperformed professional-agency English-to-Czech news translation in preserving text meaning (translation adequacy). While human translation is still rated as more fluent, CUBBITT is shown to be substantially more fluent than previous state-of-the-art systems. Moreover, most participants of a Translation Turing test struggle to distinguish CUBBITT translations from human translations. This work approaches the quality of human translation and even surpasses it in adequacy in certain circumstances. This suggests that deep learning may have the potential to replace humans in applications where conservation of meaning is the primary aim.

Název v anglickém jazyce

Transforming machine translation: a deep learning system reaches news translation quality comparable to human professionals

Popis výsledku anglicky

The quality of human translation was long thought to be unattainable for computer translation systems. In this study, we present a deep-learning system, CUBBITT, which challenges this view. In a context-aware blind evaluation by human judges, CUBBITT significantly outperformed professional-agency English-to-Czech news translation in preserving text meaning (translation adequacy). While human translation is still rated as more fluent, CUBBITT is shown to be substantially more fluent than previous state-of-the-art systems. Moreover, most participants of a Translation Turing test struggle to distinguish CUBBITT translations from human translations. This work approaches the quality of human translation and even surpasses it in adequacy in certain circumstances. This suggests that deep learning may have the potential to replace humans in applications where conservation of meaning is the primary aim.

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

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název periodika

Nature Communications

ISSN

2041-1723

e-ISSN

—

Svazek periodika

11

Číslo periodika v rámci svazku

4381

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

15

Strana od-do

1-15

Kód UT WoS článku

000569891500008

EID výsledku v databázi Scopus

2-s2.0-85090052524