A Comparative Analysis of Japanese-English Machine Translation Outputs Using Neural and Statistical Systems: Google Translate vs. Systran - Ramlan, Muhammad Nadzif (Universitas Pendidikan Indonesia (Indonesia University of Education), 2024)
This study explores the effectiveness and accuracy of Google Translate and Systran in translating Japanese to English, focusing on syntactic and semantic error patterns. It evaluates the translation quality of the text 音楽のテンポが経済的意 思決定に及ぼす影響 “Ongaku no tenpo ga keizaiteki ishi kettei ni oyobosu eikyou” (The Effect of Music Tempo on Economic Decision-Making) by Kobayashi, Fujikawa, and Foo (2012). The methodology employs a qualitative approach, categorizing errors based on syntactic and semantic criteria. Despite advancements in Neural Machine Translation (NMT), challenges remain in achieving accurate and contextually appropriate translations, particularly for complex language pairs like Japanese-English. The study highlights the persistent issues in maintaining syntactic and semantic accuracy in translations produced by Google Translate and Systran. It underscores the importance of Machine Translation Post-Editing (MTPE) to enhance translation quality. The findings reveal that while MT systems have significantly improved, human intervention remains essential to address nuanced linguistic and cultural elements. The research emphasizes the relevance of Machine- Aided Human Translation (MAHT) as a balanced approach, combining the efficiency of MT with human expertise to ensure high-quality translations. This approach is crucial for fostering better cross-cultural communication and understanding in the translation industry.