Automatic Estimation of Simultaneous Interpreter Performance
Automatic Estimation of Simultaneous Interpreter Performance
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DOI：
10.18653/v1/p18-2105
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发表时间：
2018-05
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通讯作者：
Craig Alan Stewart;Nikolai Vogler;Junjie Hu;Jordan L. Boyd-Graber;Graham Neubig
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作者：
Craig Alan Stewart;Nikolai Vogler;Junjie Hu;Jordan L. Boyd-Graber;Graham Neubig
同声传译，即实时口语翻译，既极具挑战性，又对体力要求很高。预测口译员信心和口译信息充分性的方法具有许多潜在的应用，例如在计算机辅助口译界面或教学工具中。我们提出了通过建立机器翻译输出质量估计（QE）的现有方法来预测同声传译员性能的任务。在三种语言对的五种设置的实验中，我们扩展了 QE 管道来估计解释器性能（由 METEOR 评估指标近似），并提出反映解释策略和评估措施的新特征，以进一步提高预测准确性。
Simultaneous interpretation, translation of the spoken word in real-time, is both highly challenging and physically demanding. Methods to predict interpreter confidence and the adequacy of the interpreted message have a number of potential applications, such as in computer-assisted interpretation interfaces or pedagogical tools. We propose the task of predicting simultaneous interpreter performance by building on existing methodology for quality estimation (QE) of machine translation output. In experiments over five settings in three language pairs, we extend a QE pipeline to estimate interpreter performance (as approximated by the METEOR evaluation metric) and propose novel features reflecting interpretation strategy and evaluation measures that further improve prediction accuracy.