@inproceedings{sawkar-etal-2025-lpi,
title = "{LPI}-{RIT} at {L}e{W}i{D}i-2025: Improving Distributional Predictions via Metadata and Loss Reweighting with {D}is{C}o",
author = "Sawkar, Mandira and
Shetty, Samay U. and
Pandita, Deepak and
Weerasooriya, Tharindu Cyril and
Homan, Christopher M.",
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-1.17/",
pages = "196--207",
ISBN = "979-8-89176-350-0",
abstract = "The Learning With Disagreements (LeWiDi) 2025 shared task aims to model annotator disagreement through soft label distribution prediction and perspectivist evaluation, which focuses on modeling individual annotators. We adapt DisCo (Distribution from Context), a neural architecture that jointly models item-level and annotator-level label distributions, and present detailed analysis and improvements. In this paper, we extend DisCo by introducing annotator metadata embeddings, enhancing input representations, and multi-objective training losses to capture disagreement patterns better. Through extensive experiments, we demonstrate substantial improvements in both soft and perspectivist evaluation metrics across three datasets. We also conduct in-depth calibration and error analyses that reveal when and why disagreement-aware modeling improves. Our findings show that disagreement can be better captured by conditioning on annotator demographics and by optimizing directly for distributional metrics, yielding consistent improvements across datasets."
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%0 Conference Proceedings
%T LPI-RIT at LeWiDi-2025: Improving Distributional Predictions via Metadata and Loss Reweighting with DisCo
%A Sawkar, Mandira
%A Shetty, Samay U.
%A Pandita, Deepak
%A Weerasooriya, Tharindu Cyril
%A Homan, Christopher M.
%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-lpi
%X The Learning With Disagreements (LeWiDi) 2025 shared task aims to model annotator disagreement through soft label distribution prediction and perspectivist evaluation, which focuses on modeling individual annotators. We adapt DisCo (Distribution from Context), a neural architecture that jointly models item-level and annotator-level label distributions, and present detailed analysis and improvements. In this paper, we extend DisCo by introducing annotator metadata embeddings, enhancing input representations, and multi-objective training losses to capture disagreement patterns better. Through extensive experiments, we demonstrate substantial improvements in both soft and perspectivist evaluation metrics across three datasets. We also conduct in-depth calibration and error analyses that reveal when and why disagreement-aware modeling improves. Our findings show that disagreement can be better captured by conditioning on annotator demographics and by optimizing directly for distributional metrics, yielding consistent improvements across datasets.
%U https://aclanthology.org/2025.nlperspectives-1.17/
%P 196-207
Markdown (Informal)
[LPI-RIT at LeWiDi-2025: Improving Distributional Predictions via Metadata and Loss Reweighting with DisCo](https://aclanthology.org/2025.nlperspectives-1.17/) (Sawkar et al., NLPerspectives 2025)
ACL