Gustika Dayama Putri
Faculty of Languages and Arts, State University of Padang

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

TYPES OF ERRORS FOUND IN GOOGLE TRANSLATION: A MODEL OF MT EVALUATION Gustika Dayama Putri; Ardi Havid
Proceedings of ISELT FBS Universitas Negeri Padang Vol 3 (2015): Proceedings of 3nd International Seminar on English Language Teaching (ISELT)
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (108.345 KB)

Abstract

Machine translation has become the trending topic in translation studies. The ease and practicality offered by machine translation make it become the prominent choice in translating source texts. Nowadays, machine translation has also spread its influences in English education. It is utilized as a tool to teach foreign language to language learners, especially in translation course. Google Translate which has shown the best accuracy among any other machine translations is proved to be the most used MT. However, the quality of its output is not guaranteed thoroughly since translation errors occasionally appear in Google translation. Apart from a numerous lexical resources, the understanding of both source and target language is significantly needed to produce a good translation. This paper presents the results of a research study focusing on the types of Google translation errors found in the English translation of Indonesian folklores. The analysis of Google translation errors not merely shows what types of errors produced by Google Translate but also illustrates the implications of these errors to the message delivered in the translation, which can be used as didactic media to increase the critical thinking of language learners related to language use. The study found that there are 4 types of errors in the English translation of Indonesian folklores produced by Google Translate such as ‘incorrect words’, ‘missing words’, ‘word order’, and ‘unknown words’ errors. These results are hoped to sharpen up linguistic awareness of language learners, particularly related to the choice of words and the grammatical structure. Key words: Machine Translation, Translation Errors, Google Translate, Folklore