A Wikipedia-based Corpus for Contextualized Machine Translation

We describe a corpus for and experiments in target-contextualized machine translation (MT), in which we incorporate language models from target-language documents that are comparable in nature to the source documents. This corpus comprises (i) a set of curated English Wikipedia articles describing news events along with (ii) their comparable Spanish counterparts, (iii) a number of the Spanish source articles cited within them, and (iv) English reference translations of all the Spanish data. In experiments, we evaluate the effect on translation quality when including language models built over these English documents and interpolated with other, separately-derived, more general language model sources. We find that even under this simplistic baseline approach, we achieve significant improvements as measured by BLEU score.