Abstract
A new issue frame annotated corpus of online discussions is introduced, and the paper explores the transferability of issue frame detection models from newswire and social media to discussion fora using multi-task and adversarial training with only unlabeled data.
In online discussion fora, speakers often make arguments for or against something, say birth control, by highlighting certain aspects of the topic. In social science, this is referred to as issue framing. In this paper, we introduce a new issue frame annotated corpus of online discussions. We explore to what extent models trained to detect issue frames in newswire and social media can be transferred to the domain of discussion fora, using a combination of multi-task and adversarial training, assuming only unlabeled training data in the target domain.
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