We first observe a potential weakness of continuous vector representations of
symbols in neural machine translation. That is, the continuous vector
representation, or a word embedding vector, of a symbol encodes multiple
dimensions of similarity, equivalent to encoding more than one meaning of the
word. This has the consequence that the encoder and decoder recurrent networks
in neural machine translation need to spend substantial amount of their
capacity in disambiguating source and target words based on the context which
is defined by a source sentence. Based on this observation, in this paper we
propose to contextualize the word embedding vectors using a nonlinear
bag-of-words representation of the source sentence. Additionally, we propose to
represent special tokens (such as numbers, proper nouns and acronyms) with
typed symbols to facilitate translating those words that are not well-suited to
be translated via continuous vectors. The experiments on En-Fr and En-De reveal
that the proposed approaches of contextualization and symbolization improves
the translation quality of neural machine translation systems significantly.

本文首次观察了神经机器翻译中不断向量表示的潜在弱点，基于此提出了利用非线性词袋表示法和类型化符号来提高对源语言和目标语言的区分度，从而显著提高了神经机器翻译系统的翻译质量。