Cross-lingual and cross-country approaches to argument component detection: a comparative study.

AbstractArgument mining in multilingual settings has rarely been investigated, due to the lack of annotated resources and to the inherent difficulty of the task. We benchmark the performance of models on cross-lingual and cross-country argument component detection, focusing on political data from the US and France. To do so, we introduce FrenchPolArg, a corpus of argumentative political discourse in French, and we automatically translate already existing US-English resources. We benchmark three different cross-lingual and cross-country pipelines, and compare their results to find the best-performing one. We obtain promising results to be integrated in semi-automatic annotation workflows to reduce the time and cost of annotations.