Recurrent models have been dominating the field of neural machine translation
(NMT) for the past few years. Transformers \citep{vaswani2017attention}, have
radically changed it by proposing a novel architecture that relies on a
feed-forward backbone and self-attention mechanism. Although Transformers are
powerful, they could fail to properly encode sequential/positional information
due to their non-recurrent nature. To solve this problem, position embeddings
are defined exclusively for each time step to enrich word information. However,
such embeddings are fixed after training regardless of the task and the word
ordering system of the source or target language.
In this paper, we propose a novel architecture with new position embeddings
depending on the input text to address this shortcoming by taking the order of
target words into consideration. Instead of using predefined position
embeddings, our solution generates new embeddings to refine each word's
position information. Since we do not dictate the position of source tokens and
learn them in an end-to-end fashion, we refer to our method as dynamic position
encoding (DPE). We evaluated the impact of our model on multiple datasets to
translate from English into German, French, and Italian and observed meaningful
improvements in comparison to the original Transformer.

本研究提出了一种新的动态位置编码（DPE）方法，通过新的位置嵌入来纠正目标单词的位置信息，相较于传统 Transformers 在英德法意四种翻译任务中取得了显著的性能提升。