In this paper, we investigate how to achieve robust performance for argumentation mining across domains, focusing on the classification of the argumentativeness of text segments.
we approach the data side of this problem, namely, we apply distant supervision to automatically create a large annotated corpus with argumentative and non-argumentative text segments from several domains, exploiting metadata from the online debate portal idebate.org.