  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
language models, our method obtains an improvement of $11.66$ average $F_1$ on
$16$ datasets when fine-tuned on only $20$ training samples per dataset.Our
code is available at: https://github.com/UBC-NLP/infodcl
