Social localisation is a kind of community action, which matches communities and the content
they need, and supports their localisation efforts. The goal of social localisation-based statistical
machine translation (SL-SMT) is to support and bridge global communities exchanging
any type of digital content across different languages and cultures. Trommons is an open
platform maintained by The Rosetta Foundation to connect non-profit translation projects and
organisations with the skills and interests of volunteer translators, where they can translate,
post-edit or proofread different types of documents. Using Trommons as the experimental
platform, this paper focuses on domain adaptation techniques to augment SL-SMT to facilitate
translators/post-editors. Specifically, the Cross Entropy Difference algorithm is used to adapt
Europarl data to the social localisation data. Experimental results on English–Spanish show
that the domain adaptation techniques can significantly improve translation performance by
6.82 absolute BLEU points and 5.99 absolute TER points compared to the baseline.