Faculty of Mathematics and Physics, Charles University, Prague, 121 16, Czech Republic.
Ludwig Cancer Research Oxford, University of Oxford, Oxford, OX1 2JD, UK.
Nat Commun. 2020 Sep 1;11(1):4381. doi: 10.1038/s41467-020-18073-9.
The quality of human translation was long thought to be unattainable for computer translation systems. In this study, we present a deep-learning system, CUBBITT, which challenges this view. In a context-aware blind evaluation by human judges, CUBBITT significantly outperformed professional-agency English-to-Czech news translation in preserving text meaning (translation adequacy). While human translation is still rated as more fluent, CUBBITT is shown to be substantially more fluent than previous state-of-the-art systems. Moreover, most participants of a Translation Turing test struggle to distinguish CUBBITT translations from human translations. This work approaches the quality of human translation and even surpasses it in adequacy in certain circumstances.This suggests that deep learning may have the potential to replace humans in applications where conservation of meaning is the primary aim.
长期以来，人们一直认为，人类翻译的质量是计算机翻译系统无法企及的。在这项研究中，我们提出了一个深度学习系统 CUBBITT，它挑战了这一观点。在人类评委的语境感知盲评中，CUBBITT 在保留文本意义（翻译充分性）方面明显优于专业机构的英语到捷克语新闻翻译。虽然人类翻译仍然被认为更流畅，但 CUBBITT 比以前的最先进系统明显更流畅。此外，翻译图灵测试的大多数参与者都难以区分 CUBBITT 的翻译和人工翻译。这项工作接近人类翻译的质量，在某些情况下甚至在充分性方面超越了人类翻译。这表明，在以保留意义为主要目标的应用中，深度学习可能有潜力取代人类。
