mlsanityData to 7 Oct 2026

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

arXiv 2501.09451 ↗First posted 16 Jan 2025cs.CLcited by 0

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 27 of its 51 references that are in the map. First posted on arXiv 16 Jan 2025.

Builds on

27 of 51 references.

References · 27

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  3. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  4. 2020Scaling Laws for Neural Language Modelscited by 6,411
  5. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
  6. 2013Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computationcited by 2,031
  7. 2018Relational inductive biases, deep learning, and graph networkscited by 1,341
  8. 2018Averaging Weights Leads to Wider Optima and Better Generalizationcited by 1,194
  9. 2016Deep Biaffine Attention for Neural Dependency Parsingcited by 334
  10. 2023NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classificationcited by 207
  11. 2018Constituency Parsing with a Self-Attentive Encodercited by 196
  12. 2022Pure Transformers are Powerful Graph Learnerscited by 188
15 more
  1. 2017Structured Attention Networkscited by 95
  2. 2019Graph Transformer for Graph-to-Sequence Learningcited by 82
  3. 2017A Minimal Span-Based Neural Constituency Parsercited by 59
  4. 2016Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representationscited by 55
  5. 2020Efficient Second-Order TreeCRF for Neural Dependency Parsingcited by 48
  6. 2015Transition-Based Dependency Parsing with Stack Long Short-Term Memorycited by 45
  7. 2021Systematic Generalization with Edge Transformerscited by 38
  8. 2021A Span-Based Model for Joint Overlapped and Discontinuous Named Entity Recognitioncited by 33
  9. 2020Recursive Non-Autoregressive Graph-to-Graph Transformer for Dependency Parsing with Iterative Refinementcited by 20
  10. 2020Second-Order Neural Dependency Parsing with Message Passing and End-to-End Trainingcited by 17
  11. 2020Improving Constituency Parsing with Span Attentioncited by 17
  12. 2021Dependency Parsing as MRC-based Span-Span Predictioncited by 13
  13. 2021Headed-Span-Based Projective Dependency Parsingcited by 11
  14. 2023Joint Entity and Relation Extraction with Span Pruning and Hypergraph Neural Networkscited by 11
  15. 2017Dependency Parsing with Dilated Iterated Graph CNNscited by 1

Built on by

Not cited yet.