Syntactic Substitutability as Unsupervised Dependency Syntax
This provides a theory-agnostic approach to syntax parsing for NLP researchers, though it is incremental as it builds on existing language model methods.
The paper tackles the problem of inducing syntactic dependency structures from language models by modeling syntactic substitutability, achieving 79.5% recall on long-distance subject-verb agreement compared to 8.9% with a previous method.
Syntax is a latent hierarchical structure which underpins the robust and compositional nature of human language. In this work, we explore the hypothesis that syntactic dependencies can be represented in language model attention distributions and propose a new method to induce these structures theory-agnostically. Instead of modeling syntactic relations as defined by annotation schemata, we model a more general property implicit in the definition of dependency relations, syntactic substitutability. This property captures the fact that words at either end of a dependency can be substituted with words from the same category. Substitutions can be used to generate a set of syntactically invariant sentences whose representations are then used for parsing. We show that increasing the number of substitutions used improves parsing accuracy on natural data. On long-distance subject-verb agreement constructions, our method achieves 79.5% recall compared to 8.9% using a previous method. Our method also provides improvements when transferred to a different parsing setup, demonstrating that it generalizes.