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    <abstract>Label projection is an effective technique for cross-lingual transfer, extending span-annotated datasets from a high-resource language to low-resource ones. Most approaches perform label projection as a separate step after machine translation, and prior work that combines the two reports degraded translation quality. We re-evaluate this claim with LabelPigeon, a novel framework that jointly performs translation and label projection via XML tags. We design a direct evaluation scheme for label projection, and find that LabelPigeon outperforms baselines and actively improves translation quality in 11 languages. We further assess translation quality across 203 languages and varying annotation complexity, finding consistent improvement attributed to additional fine-tuning. Finally, across 27 languages and three downstream tasks, we report substantial gains in cross-lingual transfer over comparable work, up to +40.2 F1 on NER. Overall, our results demonstrate that XML-tagged label projection provides effective and efficient label transfer without compromising translation quality.</abstract>
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%0 Conference Proceedings
%T Just Use XML: Revisiting Joint Translation and Label Projection
%A K, Thennal D.
%A Biemann, Chris
%A Hatzel, Hans Ole
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F k-etal-2026-just
%X Label projection is an effective technique for cross-lingual transfer, extending span-annotated datasets from a high-resource language to low-resource ones. Most approaches perform label projection as a separate step after machine translation, and prior work that combines the two reports degraded translation quality. We re-evaluate this claim with LabelPigeon, a novel framework that jointly performs translation and label projection via XML tags. We design a direct evaluation scheme for label projection, and find that LabelPigeon outperforms baselines and actively improves translation quality in 11 languages. We further assess translation quality across 203 languages and varying annotation complexity, finding consistent improvement attributed to additional fine-tuning. Finally, across 27 languages and three downstream tasks, we report substantial gains in cross-lingual transfer over comparable work, up to +40.2 F1 on NER. Overall, our results demonstrate that XML-tagged label projection provides effective and efficient label transfer without compromising translation quality.
%U https://aclanthology.org/2026.findings-acl.1721/
%P 34461-34478

Markdown (Informal)

[Just Use XML: Revisiting Joint Translation and Label Projection](https://aclanthology.org/2026.findings-acl.1721/) (K et al., Findings 2026)

• Just Use XML: Revisiting Joint Translation and Label Projection (K et al., Findings 2026)

ACL

• Thennal D K, Chris Biemann, and Hans Ole Hatzel. 2026. Just Use XML: Revisiting Joint Translation and Label Projection. In Findings of the Association for Computational Linguistics: ACL 2026, pages 34461–34478, San Diego, California, United States. Association for Computational Linguistics.