Neural Machine Translation (NMT) models generally perform translation using a
fixed-size lexical vocabulary, which is an important bottleneck on their
generalization capability and overall translation quality. The standard
approach to overcome this limitation is to segment words into subword units,
typically using some external tools with arbitrary heuristics, resulting in
vocabulary units not optimized for the translation task. Recent studies have
shown that the same approach can be extended to perform NMT directly at the
level of characters, which can deliver translation accuracy on-par with
subword-based models, on the other hand, this requires relatively deeper
networks. In this paper, we propose a more computationally-efficient solution
for character-level NMT which implements a hierarchical decoding architecture
where translations are subsequently generated at the level of words and
characters. We evaluate different methods for open-vocabulary NMT in the
machine translation task from English into five languages with distinct
morphological typology, and show that the hierarchical decoding model can reach
higher translation accuracy than the subword-level NMT model using
significantly fewer parameters, while demonstrating better capacity in learning
longer-distance contextual and grammatical dependencies than the standard
character-level NMT model.

本文提出了一种更高效的字符级神经机器翻译解码方法，使用词和字符级别的分层解码结构，能够优化机器翻译性能并学习更长的上下文和语法依赖。