Position encoding (PE), an essential part of self-attention networks (SANs),
is used to preserve the word order information for natural language processing
tasks, generating fixed position indices for input sequences. However, in
cross-lingual scenarios, e.g. machine translation, the PEs of source and target
sentences are modeled independently. Due to word order divergences in different
languages, modeling the cross-lingual positional relationships might help SANs
tackle this problem. In this paper, we augment SANs with \emph{cross-lingual
position representations} to model the bilingually aware latent structure for
the input sentence. Specifically, we utilize bracketing transduction grammar
(BTG)-based reordering information to encourage SANs to learn bilingual
diagonal alignments. Experimental results on WMT'14 English$\Rightarrow$German,
WAT'17 Japanese$\Rightarrow$English, and WMT'17 Chinese$\Leftrightarrow$English
translation tasks demonstrate that our approach significantly and consistently
improves translation quality over strong baselines. Extensive analyses confirm
that the performance gains come from the cross-lingual information.

本文使用跨语言位置表示法，将自注意力网络中的位置编码扩展为双语感知潜在结构，利用括号转换语法（BTG）基于重排序信息，以鼓励自注意力网络学习双语对角线对齐，并通过 WMT'14 英语⇒德语，WAT'17 日语⇒英语，WMT'17 中英互译任务的实验结果验证，显著且一致地改善了翻译质量。