  Implicit discourse relation classification is one of the most challenging and
important tasks in discourse parsing, due to the lack of connective as strong
linguistic cues. A principle bottleneck to further improvement is the shortage
of training data (ca.~16k instances in the PDTB). Shi et al. (2017) proposed to
acquire additional data by exploiting connectives in translation: human
translators mark discourse relations which are implicit in the source language
explicitly in the translation. Using back-translations of such explicitated
connectives improves discourse relation parsing performance. This paper
addresses the open question of whether the choice of the translation language
matters, and whether multiple translations into different languages can be
effectively used to improve the quality of the additional data.
