The Evaluation of Machine Translation and Human Translation on YouTube: A Comparative Study
The Evaluation of Machine Translation and Human Translation
Abstract
This study compared machine translation and human translation quality, particularly in the context of selected "Sukses Daily" videos on YouTube. In achieving these goals, this research used mixed-method approaches. It was analyzed based on the Translation Quality Assessment (TQA) model by Nababan et. al. (2012): accuracy, acceptability, and readability. Meanwhile, the data was collected through questionnaires distributed and opinions were sought through interviews with the raters. The analysis showed that human translation generally outperformed machine translation in accuracy, acceptability, and readability. Machine translation scored an average of 1.48 out of 3 for accuracy, 1.81 out of 3 for acceptability, and 1.99 out of 3 for readability, indicating poor quality. On the other hand, human translation scored an average of 2.65 out of 3 for accuracy, 2.52 out of 3 for acceptability, and 2.72 out of 3 for readability, indicating good quality. The findings could contribute to the advancement of translation knowledge and encourage further research in the field.
Keywords: Translation Quality Assessment, Machine Translation, Human Translation.
Author Biographies
Muh. Ikram, State Islamic University of Alauddin Makassar
3.png)
Scopus ID:_3.png)
ORCHID ID:3.png)
G-Scholar ID:3.png)
SINTA ID:
Muhammad Nur Akbar Rasyid, State Islamic University of Alauddin Makassar
- Scopus ID:
- ORCHID ID: 0000-0003-2951-9183
- G-Scholar ID: T6z39NgAAAAJ&hl
- SINTA ID: 6200629
Muh. Syukri, State Islamic University of Alauddin Makassar
- Scopus ID:
- ORCHID ID: 0000-0003-2507-9723
- G-Scholar ID: SmHOWOcAAAAJ&hl
- SINTA ID: 6718804
Downloads
Published
How to Cite
Issue
Section
License
Authors who publish with this journal agree to the following terms:
1) Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
2) Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
3)Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
