A Two-Stage Method for Chinese AMR Parsing
This addresses AMR parsing for Chinese language processing, but it is incremental as it builds on existing methods with specific improvements.
The paper tackles Chinese Abstract Meaning Representation (AMR) parsing by proposing a two-stage method with alignment generation, achieving Align-Smatch F1 scores of 0.7756 and 0.7074 on test sets.
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.