Assessing Human Post-Editing Efforts to Compare the Performance of Three Machine Translation Engines for English to Russian Translation of Cochrane Plain Language Health Information: Results of a Randomised Comparison
| Autor: | Azat Gabdrakhmanov, Liliya Eugenevna Ziganshina, Juliane Ried, Ekaterina V. Yudina |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
language translation
Machine translation Computer Networks and Communications Process (engineering) Computer science media_common.quotation_subject DeepL computer.software_genre machine translation 03 medical and health sciences 0302 clinical medicine 030225 pediatrics Feature (machine learning) Quality (business) 030212 general & internal medicine Language translation Plain language media_common machine translation quality lcsh:T58.5-58.64 business.industry lcsh:Information technology Communication Cochrane plain language summaries Russian language volunteer translation Google Translate Human-Computer Interaction Workflow health domain post-editing Cochrane Russia Artificial intelligence Microsoft Translator business computer Quality assurance Natural language processing |
| Zdroj: | Informatics, Vol 8, Iss 9, p 9 (2021) Informatics Volume 8 Issue 1 |
| ISSN: | 2227-9709 |
| Popis: | Cochrane produces independent research to improve healthcare decisions. It translates its research summaries into different languages to enable wider access, relying largely on volunteers. Machine translation (MT) could facilitate efficiency in Cochrane’s low-resource environment. We compared three off-the-shelf machine translation engines (MTEs)—DeepL, Google Translate and Microsoft Translator—for Russian translations of Cochrane plain language summaries (PLSs) by assessing the quantitative human post-editing effort within an established translation workflow and quality assurance process. 30 PLSs each were pre-translated with one of the three MTEs. Ten volunteer translators post-edited nine randomly assigned PLSs each—three per MTE—in their usual translation system, Memsource. Two editors performed a second editing step. Memsource’s Machine Translation Quality Estimation (MTQE) feature provided an artificial intelligence (AI)-powered estimate of how much editing would be required for each PLS, and the analysis feature calculated the amount of human editing after each editing step. Google Translate performed the best with highest average quality estimates for its initial MT output, and the lowest amount of human post-editing. DeepL performed slightly worse, and Microsoft Translator worst. Future developments in MT research and the associated industry may change our results. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|