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Translation Quality and Effort Prediction in Professional Machine Translation Post-Editing

机译:专业机器翻译后翻译质量和精力预测

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The focus of this controlled eye-tracking and key-logging study is to analyze the behaviour of translation professionals at the European Commission's Directorate-General for Translation (DGT) when detecting and correcting errors in neural machine translated texts (NMT) and their post-edited versions (NMTPE). The experiment was informed by quality analyses of an authentic DGT parallel corpus (Vardaro, Schaeffer, and Hansen-Schirra 2019), consisting of English source texts and corresponding German NMT, NMTPE and revisions (REV). To identify the most characteristic error categories in NMT and NMTPE, we used the automatic error annotation tool Hjerson (Popovic 2011) and the more fine-grained manual MQM framework (Lommel 2014). Results show that quality assurance measures by post-editors and revisors at the DGT are most often necessary for lexical errors. More specifically, if post-editors correct mistranslations, terminology or stylistic errors in an NMT sentence, revisors are likely to correct the same type of error in the same sentence, suggesting a certain transitivity between the NMT system and human post-editors. In this study, carried out in Translog II (Carl 2012), participants' eye movements and typing behavior for test sentences where the error categories mistranslation, terminology, function words and stylistic errors are included will be compared to control sentences without errors. 30 language professionals from the DGT post-edited 100 English-German machine translated sentences from the DGT corpus. We examine the three error types' effect on early (first fixation durations, first pass durations) and late eye movement measures (e.g., total reading time and regression path duration) and on typing behaviour. Statistical regression analyses predict the temporal, technical, and cognitive effort during the DGT post-editing and revision process which will be corelated to the recognition and correction of said error categories. In addition, the behavioural data of the DGT translation professionals will be compared to those of a group of 30 translation students. Behavioural differences in the two groups will allow for further predictions regarding the effect of expertise on the post-editing process.in.
机译:这种受控眼追溯和关键日志记录研究的重点是在检测和纠正神经计算机翻译文本(NMT)中的错误时,分析欧盟委员会的翻译专业人士在欧洲委员会的委员会(DGT)的行为。编辑版本(NMTPE)。通过正宗的DGT并行语料库(Vardaro,Schaeffer和Hansen-Schirra 2019)的质量分析来了解了该实验,包括英文来源文本和相应的德国NMT,NMTPE和修订(Rev)。要确定NMT和NMTPE中最特色的错误类别,我们使用了自动错误注释工具Hjerson(Popovic 2011)和更细粒度的手动MQM框架(Lommel 2014)。结果表明,DGT后编辑和Revisors的质量保证措施最常见于词汇错误。更具体地说,如果后编辑纠正了NMT句子中的错误误差​​,术语或风格错误,则Revisors可能在同一句子中纠正相同类型的错误,这表明NMT系统和人工后编辑之间的某种传递。在本研究中,在翻译II(Carl 2012)中进行,参与者的眼球运动和用于测试句子的键入行为,其中包括错误类别错误类别,术语,功能词和风格误差,以便控制句子而没有错误。来自DGT的30名语言专业人士从DGT语料库中被编辑的100次编辑的100个英语 - 德语机器翻译句子。我们在早期(第一固定持续时间,第一遍持续时间)和晚眼运动措施(例如,总读取时间和回归持续时间)和键入行为的三种错误类型的效果。统计回归分析预测DGT后编辑和修订过程中的时间,技术和认知努力,这将被强调到所述错误类别的识别和校正。此外,DGT翻译专业人员的行为数据将与一组30名翻译学生进行比较。两组的行为差异将允许关于专业知识对后编辑过程的影响的进一步预测。

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