%0 Conference Proceedings %T Zero-shot transfer for implicit discourse relation classification %A Kurfalı, Murathan %A Östling, Robert %Y Nakamura, Satoshi %Y Gasic, Milica %Y Zukerman, Ingrid %Y Skantze, Gabriel %Y Nakano, Mikio %Y Papangelis, Alexandros %Y Ultes, Stefan %Y Yoshino, Koichiro %S Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue %D 2019 %8 September %I Association for Computational Linguistics %C Stockholm, Sweden %F kurfali-ostling-2019-zero %X 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. %R 10.18653/v1/W19-5927 %U https://aclanthology.org/W19-5927 %U https://doi.org/10.18653/v1/W19-5927 %P 226-231