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        <title>Structural Optimization Ambiguity and Simplicity Bias in Unsupervised Neural Grammar Induction</title>
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    <abstract>Neural parameterization has significantly advanced unsupervised grammar induction. However, training these models with a traditional likelihood loss for all possible parses exacerbates two issues: 1) *structural optimization ambiguity* that arbitrarily selects one among structurally ambiguous optimal grammars despite the specific preference of gold parses, and 2) *structural simplicity bias* that leads a model to underutilize rules to compose parse trees. These challenges subject unsupervised neural grammar induction (UNGI) to inevitable prediction errors, high variance, and the necessity for extensive grammars to achieve accurate predictions. This paper tackles these issues, offering a comprehensive analysis of their origins. As a solution, we introduce *sentence-wise parse-focusing* to reduce the parse pool per sentence for loss evaluation, using the structural bias from pre-trained parsers on the same dataset.In unsupervised parsing benchmark tests, our method significantly improves performance while effectively reducing variance and bias toward overly simplistic parses. Our research promotes learning more compact, accurate, and consistent explicit grammars, facilitating better interpretability.</abstract>
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%0 Conference Proceedings
%T Structural Optimization Ambiguity and Simplicity Bias in Unsupervised Neural Grammar Induction
%A Park, Jinwook
%A Kim, Kangil
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F park-kim-2024-structural
%X Neural parameterization has significantly advanced unsupervised grammar induction. However, training these models with a traditional likelihood loss for all possible parses exacerbates two issues: 1) *structural optimization ambiguity* that arbitrarily selects one among structurally ambiguous optimal grammars despite the specific preference of gold parses, and 2) *structural simplicity bias* that leads a model to underutilize rules to compose parse trees. These challenges subject unsupervised neural grammar induction (UNGI) to inevitable prediction errors, high variance, and the necessity for extensive grammars to achieve accurate predictions. This paper tackles these issues, offering a comprehensive analysis of their origins. As a solution, we introduce *sentence-wise parse-focusing* to reduce the parse pool per sentence for loss evaluation, using the structural bias from pre-trained parsers on the same dataset.In unsupervised parsing benchmark tests, our method significantly improves performance while effectively reducing variance and bias toward overly simplistic parses. Our research promotes learning more compact, accurate, and consistent explicit grammars, facilitating better interpretability.
%U https://aclanthology.org/2024.findings-acl.898
%P 15124-15139

Markdown (Informal)

[Structural Optimization Ambiguity and Simplicity Bias in Unsupervised Neural Grammar Induction](https://aclanthology.org/2024.findings-acl.898) (Park & Kim, Findings 2024)

• Structural Optimization Ambiguity and Simplicity Bias in Unsupervised Neural Grammar Induction (Park & Kim, Findings 2024)

ACL

• Jinwook Park and Kangil Kim. 2024. Structural Optimization Ambiguity and Simplicity Bias in Unsupervised Neural Grammar Induction. In Findings of the Association for Computational Linguistics ACL 2024, pages 15124–15139, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.