Universal-Guided Grammar Induction

Yuan Gao; Weiwei Sun

Universal-Guided Grammar Induction

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Abstract

We study whether Universal Grammar syntactic principles can serve as explicit inductive biases for unsupervised grammar induction. We introduce a neuro-symbolic PCFG framework that converts hypothesized linguistic universals into rule schemata, and then learns rule probabilities from child-directed speech. We investigate three assumptions: 1) extended projection, 2) prototype theory of part-of-speech, and 3) the final-over-final constraint. Experiments show that with a much smaller hypothesis space, the universal-priored model achieves superior performance to state-of-the-art neural grammar induction models, and yields more interpretable functional category assignment.

Anthology ID:: 2026.emnlp-main.2485
Volume:: Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
Month:: October
Year:: 2026
Address:: Budapest, Hungary
Editors:: Hung-Yi Lee, Sunipa Dev, Jessy Li, Desmond Elliott
Venue:: EMNLP
SIG:: SIGDAT
Publisher:: Association for Computational Linguistics
Note:
Pages:: 56426–56438
Language:
URL:: https://aclanthology.org/2026.emnlp-main.2485/
DOI:
Bibkey:
Cite (ACL):: Yuan Gao and Weiwei Sun. 2026. Universal-Guided Grammar Induction. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 56426–56438, Budapest, Hungary. Association for Computational Linguistics.
Cite (Informal):: Universal-Guided Grammar Induction (Gao & Sun, EMNLP 2026)
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PDF:: https://aclanthology.org/2026.emnlp-main.2485.pdf

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@inproceedings{gao-sun-2026-universal,
    title = "Universal-Guided Grammar Induction",
    author = "Gao, Yuan  and
      Sun, Weiwei",
    editor = "Lee, Hung-Yi  and
      Dev, Sunipa  and
      Li, Jessy  and
      Elliott, Desmond",
    booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
    month = oct,
    year = "2026",
    address = "Budapest, Hungary",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.emnlp-main.2485/",
    pages = "56426--56438",
    ISBN = "979-8-89176-459-0",
    abstract = "We study whether Universal Grammar syntactic principles can serve as explicit inductive biases for unsupervised grammar induction. We introduce a neuro-symbolic PCFG framework that converts hypothesized linguistic universals into rule schemata, and then learns rule probabilities from child-directed speech. We investigate three assumptions: 1) extended projection, 2) prototype theory of part-of-speech, and 3) the final-over-final constraint. Experiments show that with a much smaller hypothesis space, the universal-priored model achieves superior performance to state-of-the-art neural grammar induction models, and yields more interpretable functional category assignment."
}

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%0 Conference Proceedings
%T Universal-Guided Grammar Induction
%A Gao, Yuan
%A Sun, Weiwei
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F gao-sun-2026-universal
%X We study whether Universal Grammar syntactic principles can serve as explicit inductive biases for unsupervised grammar induction. We introduce a neuro-symbolic PCFG framework that converts hypothesized linguistic universals into rule schemata, and then learns rule probabilities from child-directed speech. We investigate three assumptions: 1) extended projection, 2) prototype theory of part-of-speech, and 3) the final-over-final constraint. Experiments show that with a much smaller hypothesis space, the universal-priored model achieves superior performance to state-of-the-art neural grammar induction models, and yields more interpretable functional category assignment.
%U https://aclanthology.org/2026.emnlp-main.2485/
%P 56426-56438

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Markdown (Informal)

[Universal-Guided Grammar Induction](https://aclanthology.org/2026.emnlp-main.2485/) (Gao & Sun, EMNLP 2026)

Universal-Guided Grammar Induction (Gao & Sun, EMNLP 2026)

ACL

Yuan Gao and Weiwei Sun. 2026. Universal-Guided Grammar Induction. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 56426–56438, Budapest, Hungary. Association for Computational Linguistics.