Abstract
Neural machine translation (NMT) has set new quality standards in automatic translation, yet its effect on post-editing productivity is still pending thorough investigation. We empirically test how the inclusion of NMT, in addition to domain-specific translation memories and termbases, impacts speed and quality in professional translation of financial texts. We find that even with language pairs that have received little attention in research settings and small amounts of in-domain data for system adaptation, NMT post-editing allows for substantial time savings and leads to equal or slightly better quality.
Abstract (translated)
神经机器翻译（NMT）在自动翻译中确立了新的质量标准，但其对后编辑效率的影响仍有待深入研究。我们通过经验测试，除了特定领域的翻译记忆和术语库外，NMT的加入如何影响金融文本专业翻译的速度和质量。我们发现，即使在研究环境中很少受到关注的语言对和用于系统适应的少量域内数据，NMT后编辑也可以节省大量的时间，并带来同等或稍好的质量。
URL
https://arxiv.org/abs/1906.01685
