@inproceedings{sawkar-etal-2025-mcmaster,
title = "{M}c{M}aster at {L}e{W}i{D}i-2025: Demographic-Aware {R}o{BERT}a",
author = "Sanghani, Aadi and
Azadi, Sarvin and
Jethra, Virendra and
Welch, Charles",
editor = "Abercrombie, Gavin and
Basile, Valerio and
Frenda, Simona and
Tonelli, Sara and
Dudy, Shiran",
booktitle = "Proceedings of the The 4th Workshop on Perspectivist Approaches to NLP",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.nlperspectives-1.18/",
doi = "10.18653/v1/2025.nlperspectives-1.18",
pages = "208--218",
ISBN = "979-8-89176-350-0",
abstract = "We present our submission to the Learning With Disagreements (LeWiDi) 2025 shared task. Our team implemented a variety of BERT-based models that encode annotator meta-data in combination with text to predict soft-label distributions and individual annotator labels. We show across four tasks that a combination of demographic factors leads to improved performance, however through ablations across all demographic variables we find that in some cases, a single variable performs best. Our approach placed 4th in the overall competition."
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<abstract>We present our submission to the Learning With Disagreements (LeWiDi) 2025 shared task. Our team implemented a variety of BERT-based models that encode annotator meta-data in combination with text to predict soft-label distributions and individual annotator labels. We show across four tasks that a combination of demographic factors leads to improved performance, however through ablations across all demographic variables we find that in some cases, a single variable performs best. Our approach placed 4th in the overall competition.</abstract>
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%0 Conference Proceedings
%T McMaster at LeWiDi-2025: Demographic-Aware RoBERTa
%A Sanghani, Aadi
%A Azadi, Sarvin
%A Jethra, Virendra
%A Welch, Charles
%Y Abercrombie, Gavin
%Y Basile, Valerio
%Y Frenda, Simona
%Y Tonelli, Sara
%Y Dudy, Shiran
%S Proceedings of the The 4th Workshop on Perspectivist Approaches to NLP
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-350-0
%F sawkar-etal-2025-mcmaster
%X We present our submission to the Learning With Disagreements (LeWiDi) 2025 shared task. Our team implemented a variety of BERT-based models that encode annotator meta-data in combination with text to predict soft-label distributions and individual annotator labels. We show across four tasks that a combination of demographic factors leads to improved performance, however through ablations across all demographic variables we find that in some cases, a single variable performs best. Our approach placed 4th in the overall competition.
%R 10.18653/v1/2025.nlperspectives-1.18
%U https://aclanthology.org/2025.nlperspectives-1.18/
%U https://doi.org/10.18653/v1/2025.nlperspectives-1.18
%P 208-218
Markdown (Informal)
[McMaster at LeWiDi-2025: Demographic-Aware RoBERTa](https://aclanthology.org/2025.nlperspectives-1.18/) (Sanghani et al., NLPerspectives 2025)
ACL
- Aadi Sanghani, Sarvin Azadi, Virendra Jethra, and Charles Welch. 2025. McMaster at LeWiDi-2025: Demographic-Aware RoBERTa. In Proceedings of the The 4th Workshop on Perspectivist Approaches to NLP, pages 208–218, Suzhou, China. Association for Computational Linguistics.