借鉴人类口译经验，提升机器口译的适应性与实用性
Toward Machine Interpreting: Lessons from Human Interpreting Studies
- 从人类口译研究中提炼可迁移原则用于改进语音翻译系统
- 提出当前系统缺乏动态适应能力，难以应对真实场景变化
- 适合关注人机交互、实时翻译系统的研究人员参考
当前语音翻译系统虽已取得较高准确率，但行为模式僵化，无法像人类口译员一样灵活应对现实情境。为提升其实用性并实现类口译体验，深入理解人类口译的本质至关重要。本文从机器翻译视角回顾人类口译相关文献，兼顾操作性与定性特征，识别出对语音翻译系统开发具有启发性的原则，并论证利用最新建模技术实现这些原则的可行性。研究旨在缩小现有系统与实际使用之间的感知差距，推动真正意义上的机器口译发展。
原文摘要 · Abstract (English)
Current speech translation systems, while having achieved impressive accuracies, are rather static in their behavior and do not adapt to real-world situations in ways human interpreters do. In order to improve their practical usefulness and enable interpreting-like experiences, a precise understanding of the nature of human interpreting is crucial. To this end, we discuss human interpreting literature from the perspective of the machine translation field, while considering both operational and qualitative aspects. We identify implications for the development of speech translation systems and argue that there is great potential to adopt many human interpreting principles using recent modeling techniques. We hope that our findings provide inspiration for closing the perceived usability gap, and can motivate progress toward true machine interpreting.
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