EMNLP 2021 Perceived and Intended Sarcasm Detection with Graph Attention Networks

EMNLP User Representation Sarcasm Detection Social Graph Graph Attention Network

We are looking forward to present our paper, “Perceived and Intended Sarcasm Detection with Graph Attention Networks” from Joan Plepi and Lucie Flek in Findings EMNLP 2021.

In this work, we propose a framework jointly leveraging (1) a user context from their historical tweets together with (2) the social information from a user’s conversational neighborhood in an interaction graph, to contextualize the interpretation of the post. We use graph attention networks (GAT) over users and tweets in a conversation thread, combined with dense user history representations.

Apart from achieving state-of-the-art results on the recently published dataset of 19k Twitter users with 30K labeled tweets, adding 10M unlabeled tweets as context, our results indicate that the model contributes to interpreting the sarcastic intentions of an author more than to predicting the sarcasm perception by others.