Curated Datasets and Neural Models for Machine Translation of Informal
  Registers between Mayan and Spanish Vernaculars
Andrés Lou Juan Antonio Pérez-Ortiz Felipe Sánchez-Martínez Víctor M. Sánchez-Cartagena
The Mayan languages comprise a language family with an ancient history,
millions of speakers, and immense cultural value, that, nevertheless, remains
severely underrepresented in terms of resources and global exposure. In this
paper we develop, curate, and publicly release a set of corpora in several
Mayan languages spoken in Guatemala and Southern Mexico, which we call MayanV.
The datasets are parallel with Spanish, the dominant language of the region,
and are taken from official native sources focused on representing informal,
day-to-day, and non-domain-specific language. As such, and according to our
dialectometric analysis, they differ in register from most other available
resources. Additionally, we present neural machine translation models, trained
on as many resources and Mayan languages as possible, and evaluated exclusively
on our datasets. We observe lexical divergences between the dialects of Spanish
in our resources and the more widespread written standard of Spanish, and that
resources other than the ones we present do not seem to improve translation
performance, indicating that many such resources may not accurately capture
common, real-life language usage. The MayanV dataset is available at
https://github.com/transducens/mayanv.