  The growing interest in argument mining and computational argumentation
brings with it a plethora of Natural Language Understanding (NLU) tasks and
corresponding datasets. However, as with many other NLU tasks, the dominant
language is English, with resources in other languages being few and far
between. In this work, we explore the potential of transfer learning using the
multilingual BERT model to address argument mining tasks in non-English
languages, based on English datasets and the use of machine translation. We
show that such methods are well suited for classifying the stance of arguments
and detecting evidence, but less so for assessing the quality of arguments,
presumably because quality is harder to preserve under translation. In
addition, focusing on the translate-train approach, we show how the choice of
languages for translation, and the relations among them, affect the accuracy of
the resultant model. Finally, to facilitate evaluation of transfer learning on
argument mining tasks, we provide a human-generated dataset with more than 10k
arguments in multiple languages, as well as machine translation of the English
datasets.
