Parametric Re-Alignment of Cross-Linguistic Dependency Parsing via Transformer-Augmented Head-Driven Phrase Structure Grammars: A Multivariate Constraint-Based Framework

Abstract

Cross-linguistic dependency parsing remains a persistent challenge in computational linguistics, particularly when reconciling typologically distant languages within unified grammatical formalisms. This study introduces a constraint-based, transformer-augmented computational framework that integrates Head-Driven Phrase Structure Grammar (HPSG) principles with parametric re-alignment mechanisms to optimize cross-lingual syntactic dependency resolution. Leveraging a multivariate architecture trained on Universal Dependencies 2.13 treebanks across fourteen morphologically diverse languages, the proposed model achieves statistically significant improvements in labeled attachment scores (LAS) over baseline neural parsers. Empirical evaluations demonstrate robustness across agglutinative, fusional, and isolating language typologies. The framework's constraint propagation modules exhibit measurable reductions in long-distance dependency errors, offering systematic generalizability. These findings reposition HPSG-informed parsing within modern deep-learning paradigms, with direct implications for low-resource language processing pipelines.