Contrastive Learning of Sociopragmatic Meaning in Social Media [article]

Chiyu Zhang, Muhammad Abdul-Mageed, Ganesh Jawahar
2022 arXiv   pre-print
Recent progress in representation and contrastive learning in NLP has not widely considered the class of 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,
more » ... ss 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.
arXiv:2203.07648v2 fatcat:6zmhiogvirdlznoaqonyuesc54