Guiding AMR Parsing with Reverse Graph Linearization (2023)

Abstract: 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 https://github.com/pkunlp-icler/AMR_reverse_graph_linearization .

## introduction abstract meaning representation (amr) @xcite is a formalization of a sentence's meaning using a directed acyclic graph that abstracts away from shallow syntactic features and captures the core semantics of the sentence. amr parsing involves transforming a textual input into its amr graph, as illustrated in figure 1 . ## related work amr parsing aims to convert a textual input to an amr semantic graph @xcite . there are mainly four amr parsing strategies in previous work, two-stage approaches @xcite @xcite , graph-based approaches @xcite , transition-based approaches @xcite @xcite , sequence-to-sequence approaches @xcite @xcite @xcite @xcite . in terms of amr graph linearization, @xcite explores which linearization method is better for amr parsing, and @xcite studied how to linearize different semantic resources like srl to enhance amr parsing. some methods have also been proposed to incorporate graph information into sequence-tosequence models to compensate for the discrepancy between graph and sequence @xcite . while previous seq2seqbased amr parsing models mostly take the l2r linearization order by default, our work first explores how to leverage different graph linearization orders to enhance amr parsing. ## conclusion in this work, we propose a novel reverse graph linearization (rgl) enhanced framework to address the structure loss accumulation problem observed in the seq2seq-based amr parsing. through extensive experiments and analysis, it shows that rgl significantly mitigates the problem of structure loss accumulation and outperforms the previous state-of-the-art model on both amr 2.0 and amr 3.0 datasets, which demonstrates the effectiveness of the proposed approach.