A Two-Stage Method for Chinese AMR Parsing
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Abstract (arXiv)
In this paper, we provide a detailed description of our system at CAMRP-2022 evaluation. We firstly propose a two-stage method to conduct Chinese AMR Parsing with alignment generation, which includes Concept-Prediction and Relation-Prediction stages. Our model achieves 0.7756 and 0.7074 Align-Smatch F1 scores on the CAMR 2.0 test set and the blind-test set of CAMRP-2022 individually. We also analyze the result and the limitation such as the error propagation and class imbalance problem we conclude in the current method. Code and the trained models are released at https://github.com/PKUnlp-icler/Two-Stage-CAMRP for reproduction.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 13 references that are in the map. First posted on arXiv 29 Sep 2022.
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6 of 13 references.
References · 6
- 2020Revisiting Pre-Trained Models for Chinese Natural Language Processingcited by 262
- 2022Graph Pre-training for AMR Parsing and Generationcited by 55
- 2020Improving AMR Parsing with Sequence-to-Sequence Pre-trainingcited by 30
- 2022Inducing and Using Alignments for Transition-based AMR Parsingcited by 11
- 2021Hierarchical Curriculum Learning for AMR Parsingcited by 10
- 2022ATP: AMRize Then Parse! Enhancing AMR Parsing with PseudoAMRscited by 5
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