Scaling Graph-Based Dependency Parsing with Arc Vectorization and Attention-Based Refinement
This work addresses scalability issues in dependency parsing for NLP applications, representing a novel method rather than an incremental improvement.
The authors tackled the scalability limitations of graph-based dependency parsing by proposing a unified architecture that constructs vectors for scoring arcs and labels simultaneously, eliminating the information bottleneck of traditional two-pipeline approaches. Their model achieved state-of-the-art results in accuracy and efficiency on PTB and UD benchmarks.
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.