摘要
由一名新闻记者兼机器人与机器学习的工作人员新闻编辑每日新闻-机器翻译的新数据在一份新的报告中呈现。根据NewsRx编辑在马来西亚槟榔屿的新闻报道，研究表明，“字幕翻译一直是阻碍中国电影海外发展的一个长期因素。使用神经机器翻译(NMT)作为创新解决方案的潜力还有待研究。”新闻记者引用了Sains Mala Ysia大学的研究：“本研究旨在将Google Neural Machine Translation(GNMT)整合到中国电影的汉译英字幕翻译中，根据Pedersen的FAR模型识别GNMT生成字幕的错误，并提出编辑后的(PE)建议来解决这些错误。首先，中文字幕，人译字幕，在此基础上，对中文字幕进行了误差分析，并提出了相应的PE建议来修正这些错误，其中功能对等错误最常见(约一半)，其次是可接受性错误(约三分之一)和可读性错误(14%)。标准错误排名第一（42%），其次是严重错误（30%）和轻微错误（28%）。
Abstract
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% ).”
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