LDC2017T10

Overview edit

Variants: LDC2017T10

Associated Benchmarks

This dataset is used in 1 benchmark:

• AMR Parsing - Metrics: Smatch

Recent Benchmark Submissions

Research Papers

Recent papers with results on this dataset:

• Incorporating Graph Information in Transformer-based AMR Parsing (2023) -
• ATP: AMRize Then Parse! Enhancing AMR Parsing with PseudoAMRs (2022) -
• Graph Pre-training for AMR Parsing and Generation (2022) -
• Maximum Bayes Smatch Ensemble Distillation for AMR Parsing (2021) -
• Structure-aware Fine-tuning of Sequence-to-sequence Transformers for Transition-based AMR Parsing (2021) -
• Ensembling Graph Predictions for AMR Parsing (2021) -
• Levi Graph AMR Parser using Heterogeneous Attention (2021) -
• AMR Parsing with Action-Pointer Transformer (2021) -
• A Differentiable Relaxation of Graph Segmentation and Alignment for AMR Parsing (2020) -
• Pushing the Limits of AMR Parsing with Self-Learning (2020) -
• Transition-based Parsing with Stack-Transformers (2020) -
• Improving AMR Parsing with Sequence-to-Sequence Pre-training (2020) -
• AMR Parsing via Graph-Sequence Iterative Inference (2020) -
• Core Semantic First: A Top-down Approach for AMR Parsing (2019) -
• Broad-Coverage Semantic Parsing as Transduction (2019) -
• Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning (2019) -
• AMR Parsing as Sequence-to-Graph Transduction (2019) -
• AMR Parsing as Graph Prediction with Latent Alignment (2018) -
• Neural Semantic Parsing by Character-based Translation: Experiments with Abstract Meaning Representations (2017) -