通过源端敏感度检测翻译错误，提升非母语用户信任度。
Using Source-Side Confidence Estimation for Reliable Translation into Unfamiliar Languages
- 直接评估源端嵌入变化对目标词概率的影响
- 在错误检测上优于传统对齐方法
- 适合不熟悉目标语言的翻译使用者
我们提出一种面向非目标语言母语用户的交互式机器翻译系统，旨在通过识别可能翻译错误的词语并允许用户干预纠正，提升系统的可信度与可解释性。传统机器翻译置信度估计多聚焦于目标端，而本文提出一种无需词对齐的源端置信度估计方法，直接衡量目标词概率对源端嵌入变化的敏感度。实验表明，该方法在错误检测性能上优于传统的基于对齐的方法。
原文摘要 · Abstract (English)
We present an interactive machine translation (MT) system designed for users who are not proficient in the target language. It aims to improve trustworthiness and explainability by identifying potentially mistranslated words and allowing the user to intervene to correct mistranslations. However, confidence estimation in machine translation has traditionally focused on the target side. Whereas the conventional approach to source-side confidence estimation would have been to project target word probabilities to the source side via word alignments, we propose a direct, alignment-free approach that measures how sensitive the target word probabilities are to changes in the source embeddings. Experimental results show that our method outperforms traditional alignment-based methods at detection of mistranslations.
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