{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/improving-social-meaning-detection-with","title":"Improving Social Meaning Detection with Pragmatic Masking and Surrogate Fine-Tuning","arxiv_id":"2108.00356","date":"2021-08-01","proceeding":"WASSA (ACL) 2022 5","authors":["Chiyu Zhang","Muhammad Abdul-Mageed"],"abstract":"Masked language models (MLMs) are pre-trained with a denoising objective that is in a mismatch with the objective of downstream fine-tuning. We propose pragmatic masking and surrogate fine-tuning as two complementing strategies that exploit social cues to drive pre-trained representations toward a broad set of concepts useful for a wide class of social meaning tasks. We test our models on $15$ different Twitter datasets for social meaning detection. Our methods achieve $2.34\\%$ $F_1$ over a competitive baseline, while outperforming domain-specific language models pre-trained on large datasets. Our methods also excel in few-shot learning: with only $5\\%$ of training data (severely few-shot), our methods enable an impressive $68.54\\%$ average $F_1$. The methods are also language agnostic, as we show in a zero-shot setting involving six datasets from three different languages.","url_abs":"https://arxiv.org/abs/2108.00356v4","url_pdf":"https://arxiv.org/pdf/2108.00356v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"improving-social-meaning-detection-with","repo_url":"https://github.com/chiyuzhang94/pmlm-sft","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"few-shot-learning","task_name":"Few-Shot Learning"},{"task_slug":"multi-task-learning","task_name":"Multi-Task Learning"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2108.00356","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}