论文标题
中文AMR解析的两阶段方法
A Two-Stage Method for Chinese AMR Parsing
论文作者
论文摘要
在本文中，我们在CAMRP-2022评估中提供了系统的详细说明。我们首先提出了一种两阶段的方法，用于进行中文AMR通过对齐产生进行解析，其中包括概念预测和关系预测阶段。我们的模型在CAMR 2.0测试集上获得了0.7756和0.7074对齐的F1分数，并且单独的CAMRP-2022的盲目测试集。我们还分析了结果和限制，例如误差传播和类别不平衡问题，我们在当前方法中得出结论。代码和训练有素的模型将在https://github.com/pkunlp-icler/two-stage-camrp上发布，用于复制。
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.
