Change search
ReferencesLink to record
Permanent link

Direct link
Machine Translation Of Fictional And Non-fictional Texts: An examination of Google Translate's accuracy on translation of fictional versus non-fictional texts.
Stockholm University, Faculty of Humanities, Department of English.
2014 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

This study focuses on and tries to identify areas where machine translation can be useful by examining translated fictional and non-fictional texts, and the extent to which these different text types are better or worse suited for machine translation.  It additionally evaluates the performance of the free online translation tool Google Translate (GT). The BLEU automatic evaluation metric for machine translation was used for this study, giving a score of 27.75 BLEU value for fictional texts and 32.16 for the non-fictional texts. The non-fictional texts are samples of law documents, (commercial) company reports, social science texts (religion, welfare, astronomy) and medicine. These texts were selected because of their degree of difficulty. The non-fictional sentences are longer than those of the fictional texts and in this regard MT systems have struggled. In spite of having longer sentences, the non-fictional texts got a higher BLUE score than the fictional ones. It is speculated that one reason for the higher score of non-fictional texts might be that more specific terminology is used in these texts, leaving less room for subjective interpretation than for the fictional texts. There are other levels of meaning at work in the fictional texts that the human translator needs to capture. 

Place, publisher, year, edition, pages
2014. , 16 p.
Keyword [en]
Machine translation (MT), Fully Automatic High Quality Machine Translation, (FAHQMT), Statistical Machine Translation (SMT), phrase-based system, transfter-based, BLEU
National Category
Specific Languages
URN: urn:nbn:se:su:diva-106670OAI: diva2:737887
Available from: 2014-09-29 Created: 2014-08-14 Last updated: 2014-09-29Bibliographically approved

Open Access in DiVA

Machine Translation of Fictional and Non-fictional texts(355 kB)366 downloads
File information
File name FULLTEXT01.pdfFile size 355 kBChecksum SHA-512
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Salimi, Jonni
By organisation
Department of English
Specific Languages

Search outside of DiVA

GoogleGoogle Scholar
Total: 366 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 173 hits
ReferencesLink to record
Permanent link

Direct link