  We present in this article what we believe to be one of the first attempts at
video game machine translation. Our study shows that models trained only with
limited in-domain data surpass publicly available systems by a significant
margin, and a subsequent human evaluation reveals interesting findings in the
final translation. The first part of the article introduces some of the
challenges of video game translation, some of the existing literature, as well
as the systems and data sets used in this experiment. The last sections discuss
our analysis of the resulting translation and the potential benefits of such an
automated system. One such finding highlights the model's ability to learn
typical rules and patterns of video game translations from English into French.
Our conclusions therefore indicate that the specific case of video game machine
translation could prove very much useful given the encouraging results, the
highly repetitive nature of the work, and the often poor working conditions
that translators face in this field. As with other use cases of MT in cultural
sectors, however, we believe this is heavily dependent on the proper
implementation of the tool, which should be used interactively by human
translators to stimulate creativity instead of raw post-editing for the sake of
productivity.
