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.

本研究测试了在专业翻译中使用神经机器翻译（NMT）是否能够节省人工处理时间，并发现更好的 MT 系统确实能够减少英文到捷克文翻译过程中的修改次数，但是质量和编辑时间之间的关系并不是直接的关系，并且与短语词汇表机器翻译不同的是，BLEU 不能稳定预测时间或最终输出质量。