@inproceedings{zhang-etal-2026-paint,
title = "{PAINT}: A Framework for Multilingual Pain Representation from Medical Licensing Exams to Real-World Clinical Texts",
author = "Zhang, Boya and
Ma, Yuanchao and
Wu, Jiageng and
Chaisutyakorn, Krittaphas and
Gameiro, Rodrigo and
Han, Ahram and
Huang, Chien-Yu and
Jaiswal, Nikhil and
Leite, Marianna and
Mora-Mau, Miriam and
Moreno, Jaime Alonso Pineda and
Placino, Siena Kathleen V. and
Yamamoto, Ryohei and
Jia, Yugang and
Yang, Jie and
Celi, Leo Anthony and
Teodoro, Douglas",
editor = "Adelani, David Ifeoluwa and
Alabi, Jesujoba and
Arnett, Catherine and
Chang, Tyler and
Downey, C.M. and
Goldman, Omer and
Li, Senyu and
Miranda, Lester James Validad and
Qi, Jirui and
Stap, David and
Winata, Genta Indra and
Zhang, Xinyu Crystina",
booktitle = "Proceedings of the 6th Workshop on Multilingual Representation Learning ({MRL} 2026)",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.mrl-1.51/",
pages = "684--701",
ISBN = "979-8-89176-469-9",
abstract = "Pain is one of the most common and clinically important symptoms, reflecting disease onset, progression, and treatment response. However, pain is difficult to accurately capture and interpret from medical text, as its expression varies across languages, documentation types, and patient demographics, and is often embedded in terminology, negation, temporality, and contextual descriptions. Existing pain-related studies remain largely monolingual or limited to narrow clinical settings. In this study, we introduce PAINT, a multilingual framework for studying pain representation across medical exams and real-world clinical texts. PAINT includes nine pain-focused dataset partitions spanning English, Spanish, Portuguese, and Japanese, with clinician annotations in all four languages. We curated a structured schema covering pain relevance, pain presence, anatomical location, pain descriptors, and demographic information. To capture multilingual pain representations, we leverage large language models (LLMs) and adopt an ensemble strategy to improve performance. Ten clinicians across four languages validate the labels: the ensemble reaches macro-F1 of 0.91{--}1.00 on pain relevance and pain presence and 92{--}100{\%} accuracy on demographics, with inter-annotator {\ensuremath{\kappa}} of 0.82{--}1.00. We release the WorldMedQA-Pain ensemble labels, the extraction pipeline, validation scripts, and clinician annotation sheets for publicly licensed corpora, providing a foundation for pain-centered clinical NLP and for studying pain documentation across languages and healthcare contexts."
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<abstract>Pain is one of the most common and clinically important symptoms, reflecting disease onset, progression, and treatment response. However, pain is difficult to accurately capture and interpret from medical text, as its expression varies across languages, documentation types, and patient demographics, and is often embedded in terminology, negation, temporality, and contextual descriptions. Existing pain-related studies remain largely monolingual or limited to narrow clinical settings. In this study, we introduce PAINT, a multilingual framework for studying pain representation across medical exams and real-world clinical texts. PAINT includes nine pain-focused dataset partitions spanning English, Spanish, Portuguese, and Japanese, with clinician annotations in all four languages. We curated a structured schema covering pain relevance, pain presence, anatomical location, pain descriptors, and demographic information. To capture multilingual pain representations, we leverage large language models (LLMs) and adopt an ensemble strategy to improve performance. Ten clinicians across four languages validate the labels: the ensemble reaches macro-F1 of 0.91–1.00 on pain relevance and pain presence and 92–100% accuracy on demographics, with inter-annotator \ensuremathąppa of 0.82–1.00. We release the WorldMedQA-Pain ensemble labels, the extraction pipeline, validation scripts, and clinician annotation sheets for publicly licensed corpora, providing a foundation for pain-centered clinical NLP and for studying pain documentation across languages and healthcare contexts.</abstract>
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%0 Conference Proceedings
%T PAINT: A Framework for Multilingual Pain Representation from Medical Licensing Exams to Real-World Clinical Texts
%A Zhang, Boya
%A Ma, Yuanchao
%A Wu, Jiageng
%A Chaisutyakorn, Krittaphas
%A Gameiro, Rodrigo
%A Han, Ahram
%A Huang, Chien-Yu
%A Jaiswal, Nikhil
%A Leite, Marianna
%A Mora-Mau, Miriam
%A Moreno, Jaime Alonso Pineda
%A Placino, Siena Kathleen V.
%A Yamamoto, Ryohei
%A Jia, Yugang
%A Yang, Jie
%A Celi, Leo Anthony
%A Teodoro, Douglas
%Y Adelani, David Ifeoluwa
%Y Alabi, Jesujoba
%Y Arnett, Catherine
%Y Chang, Tyler
%Y Downey, C. M.
%Y Goldman, Omer
%Y Li, Senyu
%Y Miranda, Lester James Validad
%Y Qi, Jirui
%Y Stap, David
%Y Winata, Genta Indra
%Y Zhang, Xinyu Crystina
%S Proceedings of the 6th Workshop on Multilingual Representation Learning (MRL 2026)
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-469-9
%F zhang-etal-2026-paint
%X Pain is one of the most common and clinically important symptoms, reflecting disease onset, progression, and treatment response. However, pain is difficult to accurately capture and interpret from medical text, as its expression varies across languages, documentation types, and patient demographics, and is often embedded in terminology, negation, temporality, and contextual descriptions. Existing pain-related studies remain largely monolingual or limited to narrow clinical settings. In this study, we introduce PAINT, a multilingual framework for studying pain representation across medical exams and real-world clinical texts. PAINT includes nine pain-focused dataset partitions spanning English, Spanish, Portuguese, and Japanese, with clinician annotations in all four languages. We curated a structured schema covering pain relevance, pain presence, anatomical location, pain descriptors, and demographic information. To capture multilingual pain representations, we leverage large language models (LLMs) and adopt an ensemble strategy to improve performance. Ten clinicians across four languages validate the labels: the ensemble reaches macro-F1 of 0.91–1.00 on pain relevance and pain presence and 92–100% accuracy on demographics, with inter-annotator \ensuremathąppa of 0.82–1.00. We release the WorldMedQA-Pain ensemble labels, the extraction pipeline, validation scripts, and clinician annotation sheets for publicly licensed corpora, providing a foundation for pain-centered clinical NLP and for studying pain documentation across languages and healthcare contexts.
%U https://aclanthology.org/2026.mrl-1.51/
%P 684-701
Markdown (Informal)
[PAINT: A Framework for Multilingual Pain Representation from Medical Licensing Exams to Real-World Clinical Texts](https://aclanthology.org/2026.mrl-1.51/) (Zhang et al., MRL 2026)
ACL
- Boya Zhang, Yuanchao Ma, Jiageng Wu, Krittaphas Chaisutyakorn, Rodrigo Gameiro, Ahram Han, Chien-Yu Huang, Nikhil Jaiswal, Marianna Leite, Miriam Mora-Mau, Jaime Alonso Pineda Moreno, Siena Kathleen V. Placino, Ryohei Yamamoto, Yugang Jia, Jie Yang, Leo Anthony Celi, and Douglas Teodoro. 2026. PAINT: A Framework for Multilingual Pain Representation from Medical Licensing Exams to Real-World Clinical Texts. In Proceedings of the 6th Workshop on Multilingual Representation Learning (MRL 2026), pages 684–701, Budapest, Hungary. Association for Computational Linguistics.