首页|Researchers from University Sains Malaysia Provide Details of New Studies and Fi ndings in the Area of Machine Translation (Postediting challenges in Chinese-to -English neural machine translation of movie subtitles)
Researchers from University Sains Malaysia Provide Details of New Studies and Fi ndings in the Area of Machine Translation (Postediting challenges in Chinese-to -English neural machine translation of movie subtitles)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on machine translation are presented in a new report. According to news reporting out of Penang, Malaysia, by NewsRx editors, research stated, “Subtitle translation has been a longstandin g factor hindering the overseas development of Chinese movies. The potential of using Neural Machine Translation (NMT) as an innovative solution has yet to be s tudied.” The news reporters obtained a quote from the research from University Sains Mala ysia: “This case study aims to integrate Google Neural Machine Translation (GNMT ) into the Chinese-into-English subtitle translation of Chinese movies. The rese arch identifies errors in GNMT-generated subtitles per Pedersen’s FAR model and develops post-editing (PE) recommendations to address these errors. Firstly, the Chinese subtitles, human-translated subtitles, and GNMT-generated subtitles of a Chinese movie were collected. Then, the FAR model-based error analysis was con ducted to explore the errors’ features. Lastly, PE recommendations were proposed accordingly to modify these errors. Approximately a quarter of all subtitles co ntain errors, with functional equivalence errors the most prevalent (about half) , followed by acceptability errors (about a third) and readability errors (14% ). Regarding the severity of errors, standard errors rank first (42% ), followed by serious errors (30%) and minor errors (28% ).”
University Sains MalaysiaPenangMalay siaAsiaEmerging TechnologiesMachine LearningMachine Translation
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