We introduce and demonstrate how to effectively train multilingual machine translation models with pixel representations. We experiment with two different data settings with a variety of language and script coverage, demonstrating improved performance compared to subword embeddings. We explore various properties of pixel representations such as parameter sharing within and across scripts to better understand where they lead to positive transfer. We observe that these properties not only enable seamless cross-lingual transfer to unseen scripts, but make pixel representations more data-efficient than alternatives such as vocabulary expansion. We hope this work contributes to more extensible multilingual models for all languages and scripts.

翻译：我们提出并展示了如何利用像素表示有效训练多语言机器翻译模型。通过两种不同数据设置及多语言、多文字覆盖的实验，我们证明其性能优于子词嵌入方法。我们探究了像素表示在文字内部和跨文字间的参数共享等特性，以更深入理解其产生正向迁移的条件。研究表明，这些特性不仅能实现向未知文字的无缝跨语言迁移，还使像素表示相比词汇扩展等替代方案更具数据效率。我们希望此项工作能为构建覆盖所有语言和文字的更具扩展性的多语言模型提供助力。