%0 Conference Proceedings %T Cross-lingual Argumentation Mining: Machine Translation (and a bit of Projection) is All You Need! %A Eger, Steffen %A Daxenberger, Johannes %A Stab, Christian %A Gurevych, Iryna %Y Bender, Emily M. %Y Derczynski, Leon %Y Isabelle, Pierre %S Proceedings of the 27th International Conference on Computational Linguistics %D 2018 %8 August %I Association for Computational Linguistics %C Santa Fe, New Mexico, USA %F eger-etal-2018-cross %X Argumentation mining (AM) requires the identification of complex discourse structures and has lately been applied with success monolingually. In this work, we show that the existing resources are, however, not adequate for assessing cross-lingual AM, due to their heterogeneity or lack of complexity. We therefore create suitable parallel corpora by (human and machine) translating a popular AM dataset consisting of persuasive student essays into German, French, Spanish, and Chinese. We then compare (i) annotation projection and (ii) bilingual word embeddings based direct transfer strategies for cross-lingual AM, finding that the former performs considerably better and almost eliminates the loss from cross-lingual transfer. Moreover, we find that annotation projection works equally well when using either costly human or cheap machine translations. Our code and data are available at http://github.com/UKPLab/coling2018-xling_argument_mining. %U https://aclanthology.org/C18-1071 %P 831-844