  Automatically classifying the relation between sentences in a discourse is a
challenging task, in particular when there is no overt expression of the
relation. It becomes even more challenging by the fact that annotated training
data exists only for a small number of languages, such as English and Chinese.
We present a new system using zero-shot transfer learning for implicit
discourse relation classification, where the only resource used for the target
language is unannotated parallel text. This system is evaluated on the
discourse-annotated TED-MDB parallel corpus, where it obtains good results for
all seven languages using only English training data.
