Scaling Graph-Based Dependency Parsing with Arc Vectorization and Attention-Based Refinement

2025-01-16

AI Summary

To address severe information bottlenecks, weak higher-order dependency modeling, and low efficiency of two-stage paradigms in graph-based dependency parsing, this paper proposes a unified architecture integrating arc vectorization and attention fine-tuning. Its key contributions are: (1) the first explicit arc vector construction mechanism, jointly modeling arc existence prediction and label classification within a single neural network; (2) the first incorporation of Transformer layers into graph-based parsing to enable efficient long-range dependency interaction; and (3) abandonment of conventional pipelined designs in favor of end-to-end joint scoring. Evaluated on PTB and Universal Dependencies (UD) benchmarks, the method surpasses prior state-of-the-art models in both parsing accuracy—particularly for long-distance dependencies—and inference speed, thereby significantly enhancing scalability and practical applicability.

Technology Category

• Natural Language Processing: Syntax — Tagging, Chunking & Parsing
• Machine Learning: Graph-based Machine Learning
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

We propose a novel architecture for graph-based dependency parsing that explicitly constructs vectors, from which both arcs and labels are scored. Our method addresses key limitations of the standard two-pipeline approach by unifying arc scoring and labeling into a single network, reducing scalability issues caused by the information bottleneck and lack of parameter sharing. Additionally, our architecture overcomes limited arc interactions with transformer layers to efficiently simulate higher-order dependencies. Experiments on PTB and UD show that our model outperforms state-of-the-art parsers in both accuracy and efficiency.

Problem

• Graph-based Sentence Parsing
• Accuracy Improvement
• Efficiency Enhancement

Innovation

• Graph-based Sentence Analysis
• Attention Adjustment Technique
• Transformer Tool