Neural models have drastically advanced state of the art for machine translation (MT) between high-resource languages. Traditionally, these models rely on large amounts of training data, but many language pairs lack these resources. However, an important part of the languages in the world do not have this amount of data. Most languages from the Americas are among them, having a limited amount of parallel and monolingual data, if any. Here, we present an introduction to the interested reader to the basic challenges, concepts, and techniques that involve the creation of MT systems for these languages. Finally, we discuss the recent advances and findings and open questions, product of an increased interest of the NLP community in these languages.

翻译：神经模型已大幅提升了高资源语言间机器翻译（MT）的技术水平。传统上，这些模型依赖于大量训练数据，但许多语言对缺乏此类资源。然而，世界上相当一部分语言并不具备如此规模的数据量。美洲大多数语言便属于此类，其平行语料和单语数据即便存在也极为有限。本文旨在向感兴趣的读者介绍为这些语言构建机器翻译系统所涉及的基本挑战、概念和技术。最后，我们将讨论近期进展、发现及未解决问题，这些成果源于自然语言处理学界对这些语言日益增长的研究兴趣。