Even with the new Machine Translation (MT) platform available in Google today (Neural, as compared to the previous Statistical one in the previous years), the output is not always satisfactory. This is even more obvious in specific contexts and situations. Research has shown that the implementation of rules for the process prior to and the one that follows the input activities into an MT (often referred to as the pre-editing and post editing process) has proven to be fruitful (Gerlach, et. al., 2013; Shei, 2002). However, to the best knowledge of the researcher, no research on pre-editing rules on Indonesian input into MT has been conducted. This research is significant because it might increase efficiency and effectiveness of MT, especially for the language pair Indonesian-English. For that reason, this research intends to identify the pre-editing rules required to create a solid basis to translate Indonesian Source Text (ST) into English Target Text (TT). This research adopts the product-oriented research. The results show that in the pre-editing process, the length of the sentence, the conjunctions (subordinative and correlative), and the inappropriate ST words should be the focus of attention.

machine, pre-editing, translation

Gerlach, J., et al. (2013). Combining pre-editing and post-editing to improve SMT of user-generated content. In: O’Brien, S., Simard, M. & Specia, L. Proceedings of MT Summit XIV Workshop on Post-editing Technology and Practice. p. 45-53.

Hiraoka, Y., & Yamada, M. (2019). Pre-editing plus neural machine translation for subtitling: Effective pre-editing rules for subtitling of TED Talks. MT Summit (2): 64-72.

Miyata, R., & Fujita, A. (2017, May). Dissecting human pre-editing toward better use of off-the-shelf machine translation Systems. In Proceedings of the 20th Annual Conference of the European Association for Machine Translation (EAMT), User studies papers.

Shei, C.-C. (2002) Teaching MT through pre-editing: Three case studies. In: 6th EAMT Workshop Teaching Machine Translation. Manchester, 89-98.

Statt, N. (2016, September). Google's AI translation system is approaching human-level accuracy: But there’s still significant work to be done. The Verge. Retrieved from https://www.theverge.com/2016/9/27/13078138/google-translate-ai-machine-learning-gnmt