Contrastive Learning of Sociopragmatic Meaning in Social Media
Chiyu Zhang, Muhammad Abdul-Mageed, Ganesh Jawahar
Abstract
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, across both the general and few-shot settings. For example, compared to two popular pre-trained language models, our model obtains an improvement of 11.66 average F1 on 16 datasets when fine-tuned on only 20 training samples per dataset. We also show that our framework improves uniformity and preserves the semantic structure of representations. Our code is available at: https://github.com/UBC-NLP/infodcl
BibTeX
@inproceedings{zhang-etal-2023-contrastive,
title = "Contrastive Learning of Sociopragmatic Meaning in Social Media",
author = "Zhang, Chiyu and
Abdul-Mageed, Muhammad and
Jawahar, Ganesh",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-acl.152/",
doi = "10.18653/v1/2023.findings-acl.152",
pages = "2405--2439"
}