Contrastive Learning of Sociopragmatic Meaning in Social Media

Chiyu Zhang; Muhammad Abdul-Mageed; Ganesh Jawahar

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arXiv · 2203.07648

Contrastive Learning of Sociopragmatic Meaning in Social Media

Chiyu Zhang·Muhammad Abdul-Mageed·Ganesh Jawahar

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Abstract

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

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Chiyu Zhang, Muhammad Abdul-Mageed, Ganesh Jawahar. 2022-03-15. Contrastive Learning of Sociopragmatic Meaning in Social Media. https://arxiv.org/abs/2203.07648

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