Daems Joke, Vandepitte Sonia, Hartsuiker Robert J, Macken Lieve
Department of Translation, Interpreting and Communication, Ghent UniversityGhent, Belgium.
Department of Experimental Psychology, Ghent UniversityGhent, Belgium.
Front Psychol. 2017 Aug 2;8:1282. doi: 10.3389/fpsyg.2017.01282. eCollection 2017.
Translation Environment Tools make translators' work easier by providing them with term lists, translation memories and machine translation output. Ideally, such tools automatically predict whether it is more effortful to post-edit than to translate from scratch, and determine whether or not to provide translators with machine translation output. Current machine translation quality estimation systems heavily rely on automatic metrics, even though they do not accurately capture actual post-editing effort. In addition, these systems do not take translator experience into account, even though novices' translation processes are different from those of professional translators. In this paper, we report on the impact of machine translation errors on various types of post-editing effort indicators, for professional translators as well as student translators. We compare the impact of MT quality on a product effort indicator (HTER) with that on various process effort indicators. The translation and post-editing process of student translators and professional translators was logged with a combination of keystroke logging and eye-tracking, and the MT output was analyzed with a fine-grained translation quality assessment approach. We find that most post-editing effort indicators (product as well as process) are influenced by machine translation quality, but that different error types affect different post-editing effort indicators, confirming that a more fine-grained MT quality analysis is needed to correctly estimate actual post-editing effort. Coherence, meaning shifts, and structural issues are shown to be good indicators of post-editing effort. The additional impact of experience on these interactions between MT quality and post-editing effort is smaller than expected.
翻译环境工具通过为译者提供术语列表、翻译记忆库和机器翻译输出，使他们的工作更轻松。理想情况下，此类工具能自动预测译后编辑是否比从头翻译更费力，并决定是否向译者提供机器翻译输出。当前的机器翻译质量评估系统严重依赖自动指标，尽管这些指标无法准确反映实际的译后编辑工作量。此外，这些系统没有考虑译者的经验，尽管新手的翻译过程与专业译者不同。在本文中，我们报告了机器翻译错误对专业译者和学生译者各类译后编辑工作量指标的影响。我们比较了机器翻译质量对产品工作量指标（HTER）和各类过程工作量指标的影响。通过按键记录和眼动追踪相结合的方式记录了学生译者和专业译者的翻译及译后编辑过程，并采用细粒度翻译质量评估方法对机器翻译输出进行了分析。我们发现，大多数译后编辑工作量指标（包括产品和过程指标）都受机器翻译质量的影响，但不同类型的错误会影响不同的译后编辑工作量指标，这证实需要进行更细粒度的机器翻译质量分析，才能正确估计实际的译后编辑工作量。连贯性、语义转换和结构问题被证明是译后编辑工作量的良好指标。经验对机器翻译质量和译后编辑工作量之间这些相互作用的额外影响小于预期。
