Abstract Meaning Representation (AMR) parsing aims to extract an abstract
semantic graph from a given sentence. The sequence-to-sequence approaches,
which linearize the semantic graph into a sequence of nodes and edges and
generate the linearized graph directly, have achieved good performance.
However, we observed that these approaches suffer from structure loss
accumulation during the decoding process, leading to a much lower F1-score for
nodes and edges decoded later compared to those decoded earlier. To address
this issue, we propose a novel Reverse Graph Linearization (RGL) enhanced
framework. RGL defines both default and reverse linearization orders of an AMR
graph, where most structures at the back part of the default order appear at
the front part of the reversed order and vice versa. RGL incorporates the
reversed linearization to the original AMR parser through a two-pass
self-distillation mechanism, which guides the model when generating the default
linearizations. Our analysis shows that our proposed method significantly
mitigates the problem of structure loss accumulation, outperforming the
previously best AMR parsing model by 0.8 and 0.5 Smatch scores on the AMR 2.0
and AMR 3.0 dataset, respectively. The code are available at
this https URL

提出一种新的逆序图线性化（RGL）增强框架来解决序列到序列方法在解码过程中面临的结构损失积累问题，并通过两次传递的自蒸馏机制将逆序线性化引入原始 AMR 解析器，提高模型性能并在 AMR 2.0 和 AMR 3.0 数据集上取得了显著优势。