@inproceedings{park-kim-2025-probability,
title = "Probability Distribution Collapse: A Critical Bottleneck to Compact Unsupervised Neural Grammar Induction",
author = "Park, Jinwook and
Kim, Kangil",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.1694/",
pages = "33380--33391",
ISBN = "979-8-89176-332-6",
abstract = "Unsupervised neural grammar induction aims to learn interpretable hierarchical structures from language data. However, existing models face an expressiveness bottleneck, often resulting in unnecessarily large yet underperforming grammars. We identify a core issue, *probability distribution collapse*, as the underlying cause of this limitation. We analyze when and how the collapse emerges across key components of neural parameterization and introduce a targeted solution, *collapse-relaxing neural parameterization*, to mitigate it. Our approach substantially improves parsing performance while enabling the use of significantly more compact grammars across a wide range of languages, as demonstrated through extensive empirical analysis."
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%0 Conference Proceedings
%T Probability Distribution Collapse: A Critical Bottleneck to Compact Unsupervised Neural Grammar Induction
%A Park, Jinwook
%A Kim, Kangil
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F park-kim-2025-probability
%X Unsupervised neural grammar induction aims to learn interpretable hierarchical structures from language data. However, existing models face an expressiveness bottleneck, often resulting in unnecessarily large yet underperforming grammars. We identify a core issue, *probability distribution collapse*, as the underlying cause of this limitation. We analyze when and how the collapse emerges across key components of neural parameterization and introduce a targeted solution, *collapse-relaxing neural parameterization*, to mitigate it. Our approach substantially improves parsing performance while enabling the use of significantly more compact grammars across a wide range of languages, as demonstrated through extensive empirical analysis.
%U https://aclanthology.org/2025.emnlp-main.1694/
%P 33380-33391
Markdown (Informal)
[Probability Distribution Collapse: A Critical Bottleneck to Compact Unsupervised Neural Grammar Induction](https://aclanthology.org/2025.emnlp-main.1694/) (Park & Kim, EMNLP 2025)
ACL