Using a shared vocabulary is common practice in Multilingual Neural Machine
Translation (MNMT). In addition to its simple design, shared tokens play an
important role in positive knowledge transfer, which manifests naturally when
the shared tokens refer to similar meanings across languages. However, natural
flaws exist in such a design as well: 1) when languages use different writing
systems, transfer is inhibited, and 2) even if languages use similar writing
systems, shared tokens may have completely different meanings in different
languages, increasing ambiguity. In this paper, we propose a re-parameterized
method for building embeddings to alleviate the first problem. More
specifically, we define word-level information transfer pathways via word
equivalence classes and rely on graph networks to fuse word embeddings across
languages. Our experiments demonstrate the advantages of our approach: 1) the
semantics of embeddings are better aligned across languages, 2) our method
achieves significant BLEU improvements on high- and low-resource MNMT, and 3)
only less than 1.0\% additional trainable parameters are required with a
limited increase in computational costs.

本论文提出了一种新的共享词汇的设计方法，通过定义词级别信息传递路径和使用图网络来融合跨语言的词嵌入，避免了多语言之间文字编码的问题，实验还表明该方法在高低资源多语言机器翻译中可实现显著的 BLEU 分数改进，且只需额外训练少于 1.0％的可训练参数，计算成本增加有限。