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.

本文提出了一种利用监督式序列标注器实现预测同传口译者未翻译术语的任务，分析了能够降低翻译错误和提高口译表现的智能计算机辅助口译工具，使用专门设计的任务特定特征来指示口译者翻译生词或术语的困难程度，并在NAIST的同声翻译语料库上实验证明了所提出方法的效果。

走马观花: 预测同声传译中未翻译的术语