We address the task of machine translation from an extremely low-resource
language (LRL) to English using cross-lingual transfer from a closely related
high-resource language (HRL). For many of these languages, no parallel corpora
are available, even monolingual corpora are limited and representations in
pre-trained sequence-to-sequence models are absent. These factors limit the
benefits of cross-lingual transfer from shared embedding spaces in multilingual
models. However, many extremely LRLs have a high level of lexical similarity
with related HRLs. We utilize this property by injecting character and
character-span noise into the training data of the HRL prior to learning the
vocabulary. This serves as a regularizer which makes the model more robust to
lexical divergences between the HRL and LRL and better facilitates
cross-lingual transfer. On closely related HRL and LRL pairs from multiple
language families, we observe that our method significantly outperforms the
baseline MT as well as approaches proposed previously to address cross-lingual
transfer between closely related languages. We also show that the proposed
character-span noise injection performs better than the unigram-character noise
injection.

本篇论文讨论了如何利用字符噪声注入方法，将近乎无资源语言翻译成英文，使其更能够适应高资源语言，并在多种语言家族的紧密相关语言对中得到了显著的优异表现。