This paper describes the University of Edinburgh's submissions to the WMT17
shared news translation and biomedical translation tasks. We participated in 12
translation directions for news, translating between English and Czech, German,
Latvian, Russian, Turkish and Chinese. For the biomedical task we submitted
systems for English to Czech, German, Polish and Romanian. Our systems are
neural machine translation systems trained with Nematus, an attentional
encoder-decoder. We follow our setup from last year and build BPE-based models
with parallel and back-translated monolingual training data. Novelties this
year include the use of deep architectures, layer normalization, and more
compact models due to weight tying and improvements in BPE segmentations. We
perform extensive ablative experiments, reporting on the effectivenes of layer
normalization, deep architectures, and different ensembling techniques.

该论文介绍爱丁堡大学参加 WMT17 共享新闻翻译和生物医学翻译任务的情况，使用了采用 Nematus 注意力编码器 - 解码器训练的神经机器翻译系统，并对层归一化、深度结构和不同的集成技术进行了广泛的实验。