@inproceedings{nguyen-thin-2025-twinhter,
title = "twinhter at {L}e{W}i{D}i-2025: Integrating Annotator Perspectives into {BERT} for Learning with Disagreements",
author = "Nguyen, Nguyen Huu Dang and
Thin, Dang Van",
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-1.22/",
pages = "249--255",
ISBN = "979-8-89176-350-0",
abstract = "Annotator-provided information during labeling can reflect differences in how texts are understood and interpreted, though such variation may also arise from inconsistencies or errors. To make use of this information, we build a BERT-based model that integrates annotator perspectives and evaluate it on four datasets from the third edition of the Learning With Disagreements (LeWiDi) shared task. For each original data point, we create a new (text, annotator) pair, optionally modifying the text to reflect the annotator{'}s perspective when additional information is available. The text and annotator features are embedded separately and concatenated before classification, enabling the model to capture individual interpretations of the same input. Our model achieves first place on both tasks for the Par and VariErrNLI datasets. More broadly, it performs very well on datasets where annotators provide rich information and the number of annotators is relatively small, while still maintaining competitive results on datasets with limited annotator information and a larger annotator pool."
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<abstract>Annotator-provided information during labeling can reflect differences in how texts are understood and interpreted, though such variation may also arise from inconsistencies or errors. To make use of this information, we build a BERT-based model that integrates annotator perspectives and evaluate it on four datasets from the third edition of the Learning With Disagreements (LeWiDi) shared task. For each original data point, we create a new (text, annotator) pair, optionally modifying the text to reflect the annotator’s perspective when additional information is available. The text and annotator features are embedded separately and concatenated before classification, enabling the model to capture individual interpretations of the same input. Our model achieves first place on both tasks for the Par and VariErrNLI datasets. More broadly, it performs very well on datasets where annotators provide rich information and the number of annotators is relatively small, while still maintaining competitive results on datasets with limited annotator information and a larger annotator pool.</abstract>
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%0 Conference Proceedings
%T twinhter at LeWiDi-2025: Integrating Annotator Perspectives into BERT for Learning with Disagreements
%A Nguyen, Nguyen Huu Dang
%A Thin, Dang Van
%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 nguyen-thin-2025-twinhter
%X Annotator-provided information during labeling can reflect differences in how texts are understood and interpreted, though such variation may also arise from inconsistencies or errors. To make use of this information, we build a BERT-based model that integrates annotator perspectives and evaluate it on four datasets from the third edition of the Learning With Disagreements (LeWiDi) shared task. For each original data point, we create a new (text, annotator) pair, optionally modifying the text to reflect the annotator’s perspective when additional information is available. The text and annotator features are embedded separately and concatenated before classification, enabling the model to capture individual interpretations of the same input. Our model achieves first place on both tasks for the Par and VariErrNLI datasets. More broadly, it performs very well on datasets where annotators provide rich information and the number of annotators is relatively small, while still maintaining competitive results on datasets with limited annotator information and a larger annotator pool.
%U https://aclanthology.org/2025.nlperspectives-1.22/
%P 249-255
Markdown (Informal)
[twinhter at LeWiDi-2025: Integrating Annotator Perspectives into BERT for Learning with Disagreements](https://aclanthology.org/2025.nlperspectives-1.22/) (Nguyen & Thin, NLPerspectives 2025)
ACL