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        <title>Dynamic Head Selection for Neural Lexicalized Constituency Parsing</title>
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            <namePart type="given">Wanxiang</namePart>
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    <abstract>Lexicalized parsing, which associates constituent nodes with lexical heads, has historically played a crucial role in constituency parsing by bridging constituency and dependency structures. Nevertheless, with the advent of neural networks, lexicalized structures have generally been neglected in favor of unlexicalized, span-based methods. In this paper, we revisit lexicalized parsing and propose a novel latent lexicalization framework that dynamically infers lexical heads during training without relying on predefined head-finding rules. Our method enables the model to learn lexical dependencies directly from data, offering greater adaptability across languages and datasets. Experiments on multiple treebanks demonstrate state-of-the-art or comparable performance. We also analyze the learned dependency structures, headword preferences, and linguistic biases.</abstract>
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%0 Conference Proceedings
%T Dynamic Head Selection for Neural Lexicalized Constituency Parsing
%A Hou, Yang
%A Li, Zhenghua
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F hou-li-2025-dynamic
%X Lexicalized parsing, which associates constituent nodes with lexical heads, has historically played a crucial role in constituency parsing by bridging constituency and dependency structures. Nevertheless, with the advent of neural networks, lexicalized structures have generally been neglected in favor of unlexicalized, span-based methods. In this paper, we revisit lexicalized parsing and propose a novel latent lexicalization framework that dynamically infers lexical heads during training without relying on predefined head-finding rules. Our method enables the model to learn lexical dependencies directly from data, offering greater adaptability across languages and datasets. Experiments on multiple treebanks demonstrate state-of-the-art or comparable performance. We also analyze the learned dependency structures, headword preferences, and linguistic biases.
%R 10.18653/v1/2025.acl-long.786
%U https://aclanthology.org/2025.acl-long.786/
%U https://doi.org/10.18653/v1/2025.acl-long.786
%P 16141-16155

Markdown (Informal)

[Dynamic Head Selection for Neural Lexicalized Constituency Parsing](https://aclanthology.org/2025.acl-long.786/) (Hou & Li, ACL 2025)

• Dynamic Head Selection for Neural Lexicalized Constituency Parsing (Hou & Li, ACL 2025)

ACL

• Yang Hou and Zhenghua Li. 2025. Dynamic Head Selection for Neural Lexicalized Constituency Parsing. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 16141–16155, Vienna, Austria. Association for Computational Linguistics.