We test the natural expectation that using MT in professional translation saves human processing time. The last such study was carried out by Sanchez-Torron and Koehn (2016) with phrase-based MT, artificially reducing the translation quality. In contrast, we focus on neural MT (NMT) of high quality, which has become the state-of-the-art approach since then and also got adopted by most translation companies. Through an experimental study involving over 30 professional translators for English -> Czech translation, we examine the relationship between NMT performance and post-editing time and quality. Across all models, we found that better MT systems indeed lead to fewer changes in the sentences in this industry setting. The relation between system quality and post-editing time is however not straightforward and, contrary to the results on phrase-based MT, BLEU is definitely not a stable predictor of the time or final output quality.

中文翻译：

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神经机器翻译质量和后期编辑性能

我们测试了在专业翻译中使用机器翻译节省人工处理时间的自然期望。最后一项此类研究是由 Sanchez-Torron 和 Koehn (2016) 使用基于短语的 MT 进行的，人为地降低了翻译质量。相比之下，我们专注于高质量的神经机器翻译 (NMT)，从那时起它已成为最先进的方法，也被大多数翻译公司采用。通过一项涉及 30 多名英语 -> 捷克语翻译专业翻译人员的实验研究，我们研究了 NMT 性能与后期编辑时间和质量之间的关系。在所有模型中，我们发现更好的 MT 系统确实会导致该行业环境中句子的变化更少。然而，系统质量和后期编辑时间之间的关系并不简单，而且，