This paper is an attempt to discover the main challenges in working with Baltic and Estonian languages, and to identify the most significant sources of errors generated by a SMT system trained on large-vocabulary parallel corpora from legislative domain. An immense distinction between Latvian/Lithuanian and Estonian languages causes a set of non-equivalent difficulties which we classify and compare.

In the analysis step, we move beyond automatic scores and contribute presenting a human error analysis of MT systems output that helps to determine the most prominent source of errors typical for SMT systems under consideration.