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        <title>Modal Dependency Parsing via Biaffine Attention with Self-Loop</title>
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    <abstract>A modal dependency structure represents a web of connections between events and sources of information in a document that allows for tracing of who-said-what with what levels of certainty, thereby establishing factuality in an event-centric approach. Obtaining such graphs defines the task of modal dependency parsing, which involves event and source identification along with the modal relations between them. In this paper, we propose a simple yet effective solution based on biaffine attention that specifically optimizes against the domain-specific challenges of modal dependency parsing by integrating self-loop. We show that our approach, when coupled with data augmentation by leveraging the Large Language Models to translate annotations from one language to another, outperforms the previous state-of-the-art on English and Chinese datasets by 2% and 4% respectively.</abstract>
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%0 Conference Proceedings
%T Modal Dependency Parsing via Biaffine Attention with Self-Loop
%A Chun, Jayeol
%A Xue, Nianwen
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F chun-xue-2025-modal
%X A modal dependency structure represents a web of connections between events and sources of information in a document that allows for tracing of who-said-what with what levels of certainty, thereby establishing factuality in an event-centric approach. Obtaining such graphs defines the task of modal dependency parsing, which involves event and source identification along with the modal relations between them. In this paper, we propose a simple yet effective solution based on biaffine attention that specifically optimizes against the domain-specific challenges of modal dependency parsing by integrating self-loop. We show that our approach, when coupled with data augmentation by leveraging the Large Language Models to translate annotations from one language to another, outperforms the previous state-of-the-art on English and Chinese datasets by 2% and 4% respectively.
%R 10.18653/v1/2025.findings-acl.1093
%U https://aclanthology.org/2025.findings-acl.1093/
%U https://doi.org/10.18653/v1/2025.findings-acl.1093
%P 21226-21238

Markdown (Informal)

[Modal Dependency Parsing via Biaffine Attention with Self-Loop](https://aclanthology.org/2025.findings-acl.1093/) (Chun & Xue, Findings 2025)

• Modal Dependency Parsing via Biaffine Attention with Self-Loop (Chun & Xue, Findings 2025)

ACL

• Jayeol Chun and Nianwen Xue. 2025. Modal Dependency Parsing via Biaffine Attention with Self-Loop. In Findings of the Association for Computational Linguistics: ACL 2025, pages 21226–21238, Vienna, Austria. Association for Computational Linguistics.