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Transition-based Parsing with Stack-Transformers

arXiv 2010.10669 ↗First posted 20 Oct 2020cs.CLcited by 38

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Abstract (arXiv)

Modeling the parser state is key to good performance in transition-based parsing. Recurrent Neural Networks considerably improved the performance of transition-based systems by modelling the global state, e.g. stack-LSTM parsers, or local state modeling of contextualized features, e.g. Bi-LSTM parsers. Given the success of Transformer architectures in recent parsing systems, this work explores modifications of the sequence-to-sequence Transformer architecture to model either global or local parser states in transition-based parsing. We show that modifications of the cross attention mechanism of the Transformer considerably strengthen performance both on dependency and Abstract Meaning Representation (AMR) parsing tasks, particularly for smaller models or limited training data.

Key facts. Cited by 38 papers in the map (data through 14 Oct 2026). First posted on arXiv 20 Oct 2020.

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Citing papers per year: 2021: 11; 2022: 9; 2023: 9; 2024: 5; 2025: 2; 2026: 2.

38 papers.

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  1. 2022Graph Pre-training for AMR Parsing and Generationcited by 55
  2. 2022Transformer Grammars: Augmenting Transformer Language Models with Syntactic Inductive Biases at Scalecited by 41
  3. 2021Maximum Bayes Smatch Ensemble Distillation for AMR Parsingcited by 29
  4. 2021AMR Parsing with Action-Pointer Transformercited by 25
  5. 2021Structural Guidance for Transformer Language Modelscited by 25
  6. 2021Structure-aware Fine-tuning of Sequence-to-sequence Transformers for Transition-based AMR Parsingcited by 25
  7. 2022A Two-Stream AMR-enhanced Model for Document-level Event Argument Extractioncited by 16
  8. 2021Cost-effective End-to-end Information Extraction for Semi-structured Document Imagescited by 14
  9. 2022Inducing and Using Alignments for Transition-based AMR Parsingcited by 12
  10. 2022Enhanced Knowledge Selection for Grounded Dialogues via Document Semantic Graphscited by 11
  11. 2021Hierarchical Curriculum Learning for AMR Parsingcited by 10
  12. 2023Enhancing Document-level Event Argument Extraction with Contextual Clues and Role Relevancecited by 10
26 more
  1. 2021Probabilistic, Structure-Aware Algorithms for Improved Variety, Accuracy, and Coverage of AMR Alignmentscited by 9
  2. 2021Bootstrapping Multilingual AMR with Contextual Word Alignmentscited by 8
  3. 2023An AMR-based Link Prediction Approach for Document-level Event Argument Extractioncited by 8
  4. 2024Dependency Transformer Grammars: Integrating Dependency Structures into Transformer Language Modelscited by 8
  5. 2023Incorporating Graph Information in Transformer-based AMR Parsingcited by 7
  6. 2024Text2MDT: Extracting Medical Decision Trees from Medical Textscited by 6
  7. 2022ATP: AMRize Then Parse! Enhancing AMR Parsing with PseudoAMRscited by 5
  8. 2021Discontinuous Grammar as a Foreign Languagecited by 4
  9. 2021Learning to Transpile AMR into SPARQLcited by 4
  10. 2022Improve Event Extraction via Self-Training with Gradient Guidancecited by 3
  11. 2023Approximating CKY with Transformerscited by 3
  12. 2021Levi Graph AMR Parser using Heterogeneous Attentioncited by 2
  13. 2022Cross-domain Generalization for AMR Parsingcited by 2
  14. 2023On the Challenges of Fully Incremental Neural Dependency Parsingcited by 2
  15. 2024CIC-BART-SSA: Controllable Image Captioning with Structured Semantic Augmentationcited by 2
  16. 2025Emergent Stack Representations in Modeling Counter Languages Using Transformerscited by 2
  17. 2025SoilNet: A Multimodal Multitask Model for Hierarchical Classification of Soil Horizonscited by 2
  18. 2023Structured Sentiment Analysis as Transition-based Dependency Graph Parsingcited by 1
  19. 2023AMR Parsing with Causal Hierarchical Attention and Pointerscited by 1
  20. 2024Chem-FINESE: Validating Fine-Grained Few-shot Entity Extraction through Text Reconstructioncited by 1
  21. 2024Adapting Abstract Meaning Representation Parsing to the Clinical Narrative -- the SPRING THYME parsercited by 1
  22. 2022Shift-Reduce Task-Oriented Semantic Parsing with Stack-Transformerscited by 0
  23. 2023Slide, Constrain, Parse, Repeat: Synchronous SlidingWindows for Document AMR Parsingcited by 0
  24. 2023Guiding AMR Parsing with Reverse Graph Linearizationcited by 0
  25. 2026Exploiting Pre-trained Encoder-Decoder Transformers for Sequence-to-Sequence Constituent Parsingcited by 0
  26. 2026How Reliable are AMR Parser Outputs? Uncertainty Estimation for AMR Parsingcited by 0