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        <title>Bridging Discourse Treebanks with a Unified Rhetorical Structure Parser</title>
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            <namePart type="given">Michael</namePart>
            <namePart type="family">Strube</namePart>
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        <name type="personal">
            <namePart type="given">Chloe</namePart>
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            <namePart type="given">Junyi</namePart>
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    <abstract>We introduce UniRST, the first unified RST-style discourse parser capable of handling 18 treebanks in 11 languages without modifying their relation inventories. To overcome inventory incompatibilities, we propose and evaluate two training strategies: Multi-Head, which assigns separate relation classification layer per inventory, and Masked-Union, which enables shared parameter training through selective label masking. We first benchmark mono-treebank parsing with a simple yet effective augmentation technique for low-resource settings. We then train a unified model and show that (1) the parameter efficient Masked-Union approach is also the strongest, and (2) UniRST outperforms 16 of 18 mono-treebank baselines, demonstrating the advantages of a single-model, multilingual end-to-end discourse parsing across diverse resources.</abstract>
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%0 Conference Proceedings
%T Bridging Discourse Treebanks with a Unified Rhetorical Structure Parser
%A Chistova, Elena
%Y Strube, Michael
%Y Braud, Chloe
%Y Hardmeier, Christian
%Y Li, Junyi Jessy
%Y Loaiciga, Sharid
%Y Zeldes, Amir
%Y Li, Chuyuan
%S Proceedings of the 6th Workshop on Computational Approaches to Discourse, Context and Document-Level Inferences (CODI 2025)
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-343-2
%F chistova-2025-bridging
%X We introduce UniRST, the first unified RST-style discourse parser capable of handling 18 treebanks in 11 languages without modifying their relation inventories. To overcome inventory incompatibilities, we propose and evaluate two training strategies: Multi-Head, which assigns separate relation classification layer per inventory, and Masked-Union, which enables shared parameter training through selective label masking. We first benchmark mono-treebank parsing with a simple yet effective augmentation technique for low-resource settings. We then train a unified model and show that (1) the parameter efficient Masked-Union approach is also the strongest, and (2) UniRST outperforms 16 of 18 mono-treebank baselines, demonstrating the advantages of a single-model, multilingual end-to-end discourse parsing across diverse resources.
%U https://aclanthology.org/2025.codi-1.17/
%P 197-208

Markdown (Informal)

[Bridging Discourse Treebanks with a Unified Rhetorical Structure Parser](https://aclanthology.org/2025.codi-1.17/) (Chistova, CODI 2025)

• Bridging Discourse Treebanks with a Unified Rhetorical Structure Parser (Chistova, CODI 2025)

ACL

• Elena Chistova. 2025. Bridging Discourse Treebanks with a Unified Rhetorical Structure Parser. In Proceedings of the 6th Workshop on Computational Approaches to Discourse, Context and Document-Level Inferences (CODI 2025), pages 197–208, Suzhou, China. Association for Computational Linguistics.