Enhancing Multilingual Dependency Parsing via Dynamic Linguistic Feature Extraction and Alignment

Multi-lingual dependency parsing aims to extract useful syntactic knowledge from multiple high-resource languages to improve the parsing performance of low-resource target languages. How to effectively extract linguistic commonality structures and reduce divergence feature interferences is still a key challenge. To alleviate these issues, we propose a dynamic linguistic feature extraction and alignment framework for multi-lingual dependency parsing. On the one hand, we construct multi-lingual parallel dependency trees through confidence-aware traditional parser and LLM collaboration, thus explicitly extracting cross-lingual aligned syntactic knowledge. On the other hand, we introduce a dynamic feature alignment network to selectively transfer useful high-resource syntactic features and suppress harmful ones.Experiments on four low-resource benchmarks demonstrate that our model consistently improves multi-lingual parsing baselines, achieving competitive or superior performance on all languages. Further analyses indicate that the proposed framework effectively captures transferable structural knowledge and reduces negative transfer, leading to better parsing performance in low-resource settings. Our codes and data are available at \url{github.com