  Large parallel corpora that are automatically obtained from the web,
documents or elsewhere often exhibit many corrupted parts that are bound to
negatively affect the quality of the systems and models that learn from these
corpora. This paper describes frequent problems found in data and such data
affects neural machine translation systems, as well as how to identify and deal
with them. The solutions are summarised in a set of scripts that remove
problematic sentences from input corpora.
