Abstract
The advantages of neural machine translation (NMT) have been extensively validated for offline translation of several language pairs for different domains of spoken and written language. However, research on interactive learning of NMT by adaptation to human post-edits has so far been confined to simulation experiments. We present the first user study on online adaptation of NMT to user post-edits in the domain of patent translation. Our study involves 29 human subjects (translation students) whose post-editing effort and translation quality were measured on about 4,500 interactions of a human post-editor and a machine translation system integrating an online adaptive learning algorithm. Our experimental results show a significant reduction of human post-editing effort due to online adaptation in NMT according to several evaluation metrics, including hTER, hBLEU, and KSMR. Furthermore, we found significant improvements in BLEU/TER between NMT outputs and professional translations in granted patents, providing further evidence for the advantages of online adaptive NMT in an interactive setup.
Abstract (translated)
神经机器翻译（NMT）的优点已被广泛验证用于语言和书面语言的不同领域的若干语言对的离线翻译。然而，迄今为止，通过适应人类后期编辑对NMT的交互式学习的研究仅限于模拟实验。我们在专利翻译领域向用户提供关于NMT在线改编的第一个用户研究报告。我们的研究涉及29名人类受试者（翻译学生），其编辑后的努力和翻译质量是在人类后编辑器和集成在线自适应学习算法的机器翻译系统的约4,500次交互中测量的。我们的实验结果表明，根据几个评估指标（包括hTER，hBLEU和KSMR），由于NMT中的在线适应，人类编辑后工作量显着减少。此外，我们发现NMT输出与授权专利中的专业翻译之间的BLEU / TER有显着改进，进一步证明了在线自适应NMT在交互式设置中的优势。
URL
https://arxiv.org/abs/1712.04853
