Character-based translation has several appealing advantages, but its
performance is in general worse than a carefully tuned BPE baseline. In this
paper we study the impact of character-based input and output with the
Transformer architecture. In particular, our experiments on EN-DE show that
character-based Transformer models are more robust than their BPE counterpart,
both when translating noisy text, and when translating text from a different
domain. To obtain comparable BLEU scores in clean, in-domain data and close the
gap with BPE-based models we use known techniques to train deeper Transformer
models.

本文研究了字符级别的翻译和基于 Transformer 架构的输入与输出之间的影响，并通过对 EN-DE 的实验发现字符级别 Transformer 模型比其 BPE 模型更具鲁棒性。为了在干净、领域特定的数据中获得可比的 BLEU 分数并缩小与基于 BPE 的模型之间的差距，我们采用已知的技术训练更深层的 Transformer 模型。

基于字符的 Transformer 神经机器翻译

Character-based NMT with Transformer

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。