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Modeling Frames in Argumentation

In argumentation, framing is used to emphasize a specific aspect of a controversial topic while concealing others. When talking about legalizing drugs, for instance, its economical aspect may be emphasized. In general, we call a set of arguments that focus on the same aspect a frame. An argumentative text has to serve the {``}right{''} frame(s) to convince the audience to adopt the author{'}s stance (e.g., being pro or con legalizing drugs). More specifically, an author has to choose frames that fit the audience{'}s cultural background and interests. This paper introduces frame identification, which is the task of splitting a set of arguments into non-overlapping frames. We present a fully unsupervised approach to this task, which first removes topical information and then identifies frames using clustering. For evaluation purposes, we provide a corpus with 12, 326 debate-portal arguments, organized along the frames of the debates{'} topics. On this corpus, our approach outperforms different strong baselines, achieving an F1-score of 0.28.

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