Political claim identification and categorization in a multilingual setting: First experiments
This work addresses a resource-scarce task for political analysis researchers, but it is incremental as it builds on existing methods in a new multilingual context.
The paper tackled the problem of identifying and categorizing political claims in multilingual newspaper reports by exploring cross-lingual projection strategies, finding that machine translation performed best in experiments on a German dataset covering the 2015 refugee crisis.
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).