Contrastive Learning of Sociopragmatic Meaning in Social Media. (arXiv:2203.07648v2 [cs.CL] UPDATED)

Recent progress in representation and contrastive learning in NLP has not
widely considered the class of \textit{sociopragmatic meaning} (i.e., meaning
in interaction within different language communities). To bridge this gap, we
propose a novel framework for learning task-agnostic representations
transferable to a wide range of sociopragmatic tasks (e.g., emotion, hate
speech, humor, sarcasm). Our framework outperforms other contrastive learning
frameworks for both in-domain and out-of-domain data, across both the general
and few-shot settings. For example, compared to two popular pre-trained …

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