  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.
