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Identifying the Machine Translation Error Types with the Greatest Impact on Post-editing Effort

机译：识别对后期编辑工作影响最大的机器翻译错误类型

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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.
机译：翻译环境工具为翻译人员提供术语列表，翻译记忆库和机器翻译输出，从而使翻译人员的工作更加轻松。理想情况下，此类工具会自动预测与从头开始翻译相比，后期编辑是否更省力，并确定是否向翻译人员提供机器翻译输出。当前的机器翻译质量估计系统严重依赖自动度量，即使它们不能准确地捕获实际的后期编辑工作。另外，即使新手的翻译过程与专业翻译的过程不同，这些系统也没有考虑翻译经验。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种类型的后期编辑工作量指标的影响。我们将MT质量对产品努力指标（HTER）的影响与对各种过程努力指标的影响进行了比较。结合击键记录和眼动追踪功能，记录了学生翻译人员和专业翻译人员的翻译和后期编辑过程，并使用细粒度的翻译质量评估方法对MT的输出进行了分析。我们发现大多数编辑后工作量指标（产品和过程）都受机器翻译质量的影响，但是不同的错误类型会影响不同的编辑后工作量指标，从而确认需要进行更细粒度的MT质量分析才能正确估计实际的后期编辑工作。连贯性，含义转移和结构性问题被证明是后期编辑工作的良好指标。经验对MT质量与后期编辑工作之间这些交互的额外影响小于预期。

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