The identification and classification of political claims is an important step in the analysis of political newspaper reports; however, resources for this task are few and far between. This paper explores different strategies for the cross-lingual projection of political claims analysis. We conduct experiments on a German dataset, DebateNet2.0, covering the policy debate sparked by the 2015 refugee crisis. Our evaluation involves two tasks (claim identification and categorization), three languages (German, English, and French) and two methods (machine translation -- the best method in our experiments -- and multilingual embeddings).

翻译：政治主张的识别与分类是分析政治新闻报道的重要步骤，然而支持该任务的资源十分稀缺。本文探索了跨语言迁移政治主张分析的不同策略。我们基于德国数据集DebateNet2.0开展实验，该数据集涵盖了2015年难民危机引发的政策辩论。评估涉及两个任务（主张识别与分类）、三种语言（德语、英语和法语）以及两种方法（机器翻译——本实验中的最佳方法——以及多语言嵌入）。