Lost in Interpretation: Predicting Untranslated Terminology in Simultaneous Interpretation
Lost in Interpretation: Predicting Untranslated Terminology in Simultaneous Interpretation
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DOI：
10.18653/v1/n19-1010
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发表时间：
2019-04
期刊：
影响因子：
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通讯作者：
Nikolai Vogler;Craig Alan Stewart;Graham Neubig
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文献类型：
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作者：
Nikolai Vogler;Craig Alan Stewart;Graham Neubig
同声传译，即实时地将一种语言翻译成另一种语言，是一项固有的困难和艰巨的任务。口译员面临的最大挑战之一是准确翻译专有名词，数字或其他实体等困难术语。智能计算机辅助口译（CAI）工具可以分析口语并检测口译员可能未翻译的术语，从而减少翻译错误并提高口译员的表现。在本文中，我们提出了一个任务，预测哪些术语同传译员将留下未翻译的，并检查方法，执行这项任务，使用监督序列标签。我们描述了许多明确设计的特定于任务的功能，以指示口译员何时可能难以翻译单词。在新注释版本的NAIST同声传译语料库上的实验结果（Shimizu et al.，2014）表明我们提出的方法的承诺。
Simultaneous interpretation, the translation of speech from one language to another in real-time, is an inherently difficult and strenuous task. One of the greatest challenges faced by interpreters is the accurate translation of difficult terminology like proper names, numbers, or other entities. Intelligent computer-assisted interpreting (CAI) tools that could analyze the spoken word and detect terms likely to be untranslated by an interpreter could reduce translation error and improve interpreter performance. In this paper, we propose a task of predicting which terminology simultaneous interpreters will leave untranslated, and examine methods that perform this task using supervised sequence taggers. We describe a number of task-specific features explicitly designed to indicate when an interpreter may struggle with translating a word. Experimental results on a newly-annotated version of the NAIST Simultaneous Translation Corpus (Shimizu et al., 2014) indicate the promise of our proposed method.