Dynamic Syntax Mapping: A New Approach to Unsupervised Syntax Parsing
This addresses the problem of unsupervised syntax parsing for natural language processing, offering a novel method that is adaptable to varied scenarios.
The paper tackles unsupervised syntax parsing by introducing Dynamic Syntax Mapping (DSM), which uses syntactic substitutability and language model attention to induce syntactic structures without predefined annotations, achieving a remarkable advancement in long-distance subject-verb agreement over prior methods.
The intricate hierarchical structure of syntax is fundamental to the intricate and systematic nature of human language. This study investigates the premise that language models, specifically their attention distributions, can encapsulate syntactic dependencies. We introduce Dynamic Syntax Mapping (DSM), an innovative approach for the agnostic induction of these structures. Our method diverges from traditional syntax models which rely on predefined annotation schemata. Instead, we focus on a core characteristic inherent in dependency relations: syntactic substitutability. This concept refers to the interchangeability of words within the same syntactic category at either end of a dependency. By leveraging this property, we generate a collection of syntactically invariant sentences, which serve as the foundation for our parsing framework. Our findings reveal that the use of an increasing array of substitutions notably enhances parsing precision on natural language data. Specifically, in the context of long-distance subject-verb agreement, DSM exhibits a remarkable advancement over prior methodologies. Furthermore, DSM's adaptability is demonstrated through its successful application in varied parsing scenarios, underscoring its broad applicability.