Structural Optimization Ambiguity and Simplicity Bias in Unsupervised Neural Grammar Induction
This work addresses issues in unsupervised grammar induction for natural language processing, offering a method to learn more compact and accurate grammars, though it is incremental as it builds on existing neural parameterizations.
The paper tackles the problems of structural optimization ambiguity and simplicity bias in unsupervised neural grammar induction, which cause prediction errors and high variance, by introducing sentence-wise parse-focusing to reduce the parse pool per sentence, resulting in significant performance improvements in benchmark tests while reducing variance and bias.
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) $\textit{structural optimization ambiguity}$ that arbitrarily selects one among structurally ambiguous optimal grammars despite the specific preference of gold parses, and 2) $\textit{structural simplicity bias}$ 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 $\textit{sentence-wise parse-focusing}$ 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.