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Social norms are shared rules that govern and facilitate social interaction.Violating such social norms via teasing and insults may serve to upend powerimbalances or, on the contrary reinforce solidarity and rapport inconversation, rapport which is highly situated and context-dependent. In thiswork, we investigate the task of automatically identifying the phenomena ofsocial norm violation in discourse. Towards this goal, we leverage the power ofrecurrent neural networks and multimodal information present in theinteraction, and propose a predictive model to recognize social norm violation.Using long-term temporal and contextual information, our model achieves an F1score of 0.705. Implications of our work regarding developing a social-awareagent are discussed.

Leveraging Recurrent Neural Networks for Multimodal Recognition of Social Norm Violation in Dialog