  Accessibility to historical documents is mostly limited to scholars. This is
due to the language barrier inherent in human language and the linguistic
properties of these documents. Given a historical document, modernization aims
to generate a new version of it, written in the modern version of the
document's language. Its goal is to tackle the language barrier, decreasing the
comprehension difficulty and making historical documents accessible to a
broader audience. In this work, we proposed a new neural machine translation
approach that profits from modern documents to enrich its systems. We tested
this approach with both automatic and human evaluation, and conducted a user
study. Results showed that modernization is successfully reaching its goal,
although it still has room for improvement.
