mlsanityData to 7 Oct 2026

Hierarchical Curriculum Learning for AMR Parsing

arXiv 2110.07855 ↗First posted 15 Oct 2021cs.CLcited by 10

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Abstract (arXiv)

Abstract Meaning Representation (AMR) parsing aims to translate sentences to semantic representation with a hierarchical structure, and is recently empowered by pretrained sequence-to-sequence models. However, there exists a gap between their flat training objective (i.e., equally treats all output tokens) and the hierarchical AMR structure, which limits the model generalization. To bridge this gap, we propose a Hierarchical Curriculum Learning (HCL) framework with Structure-level (SC) and Instance-level Curricula (IC). SC switches progressively from core to detail AMR semantic elements while IC transits from structure-simple to -complex AMR instances during training. Through these two warming-up processes, HCL reduces the difficulty of learning complex structures, thus the flat model can better adapt to the AMR hierarchy. Extensive experiments on AMR2.0, AMR3.0, structure-complex and out-of-distribution situations verify the effectiveness of HCL.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 18 of its 36 references that are in the map. First posted on arXiv 15 Oct 2021.

Builds on

18 of 36 references.

References · 18

  1. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  2. 2019On the Variance of the Adaptive Learning Rate and Beyondcited by 763
  3. 2019Competence-based Curriculum Learning for Neural Machine Translationcited by 201
  4. 2019AMR Parsing as Sequence-to-Graph Transductioncited by 50
  5. 2020AMR Parsing via Graph-Sequence Iterative Inferencecited by 47
  6. 2019Fine-Tuning by Curriculum Learning for Non-Autoregressive Neural Machine Translationcited by 39
  7. 2020Transition-based Parsing with Stack-Transformerscited by 37
  8. 2018AMR Parsing as Graph Prediction with Latent Alignmentcited by 36
  9. 2018Abstract Meaning Representation for Multi-Document Summarizationcited by 35
  10. 2016An Incremental Parser for Abstract Meaning Representationcited by 34
  11. 2019Broad-Coverage Semantic Parsing as Transductioncited by 32
  12. 2020Improving AMR Parsing with Sequence-to-Sequence Pre-trainingcited by 30
6 more
  1. 2020Dialogue Response Selection with Hierarchical Curriculum Learningcited by 27
  2. 2019Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learningcited by 25
  3. 2021AMR Parsing with Action-Pointer Transformercited by 25
  4. 2018Guided Neural Language Generation for Abstractive Summarization using Abstract Meaning Representationcited by 24
  5. 2019Core Semantic First: A Top-down Approach for AMR Parsingcited by 24
  6. 2020Pushing the Limits of AMR Parsing with Self-Learningcited by 16

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 5; 2023: 2; 2024: 2; 2025: 0; 2026: 1.

10 papers.

  1. 2024Let's Learn Step by Step: Enhancing In-Context Learning Ability with Curriculum Learningcited by 47
  2. 2022CLNode: Curriculum Learning for Node Classificationcited by 24
  3. 2022A Two-Stream AMR-enhanced Model for Document-level Event Argument Extractioncited by 16
  4. 2022Public Wisdom Matters! Discourse-Aware Hyperbolic Fourier Co-Attention for Social-Text Classificationcited by 11
  5. 2024HiCuLR: Hierarchical Curriculum Learning for Rhetorical Role Labeling of Legal Documentscited by 6
  6. 2022ATP: AMRize Then Parse! Enhancing AMR Parsing with PseudoAMRscited by 5
  7. 2023AMR Parsing with Causal Hierarchical Attention and Pointerscited by 1
  8. 2022A Two-Stage Method for Chinese AMR Parsingcited by 0
  9. 2023Guiding AMR Parsing with Reverse Graph Linearizationcited by 0
  10. 2026Disentangling Curriculum Learning in NLP: Towards a Unifying Taxonomycited by 0