SOTAVerified

Representing Social Media Users for Sarcasm Detection

2018-08-25EMNLP 2018Code Available0· sign in to hype

Y. Alex Kolchinski, Christopher Potts

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Abstract

We explore two methods for representing authors in the context of textual sarcasm detection: a Bayesian approach that directly represents authors' propensities to be sarcastic, and a dense embedding approach that can learn interactions between the author and the text. Using the SARC dataset of Reddit comments, we show that augmenting a bidirectional RNN with these representations improves performance; the Bayesian approach suffices in homogeneous contexts, whereas the added power of the dense embeddings proves valuable in more diverse ones.

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