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。

引导逆向图线性化的 AMR 解析

Guiding AMR Parsing with Reverse Graph Linearization

The text editing tasks, including sentence fusion, sentence splitting and
rephrasing, text simplification, and Grammatical Error Correction (GEC), share
a common trait of dealing with highly similar input and output sequences. This
area of research lies at the intersection of two well-established fields: (i)
fully autoregressive sequence-to-sequence approaches commonly used in tasks
like Neural Machine Translation (NMT) and (ii) sequence tagging techniques
commonly used to address tasks such as Part-of-speech tagging, Named-entity
recognition (NER), and similar. In the pursuit of a balanced architecture,
researchers have come up with numerous imaginative and unconventional
solutions, which we're discussing in the Related Works section. Our approach to
addressing text editing tasks is called RedPenNet and is aimed at reducing
architectural and parametric redundancies presented in specific
Sequence-To-Edits models, preserving their semi-autoregressive advantages. Our
models achieve $F_{0.5}$ scores of 77.60 on the BEA-2019 (test), which can be
considered as state-of-the-art the only exception for system combination and
67.71 on the UAGEC+Fluency (test) benchmarks.
This research is being conducted in the context of the UNLP 2023 workshop,
where it was presented as a paper as a paper for the Shared Task in Grammatical
Error Correction (GEC) for Ukrainian. This study aims to apply the RedPenNet
approach to address the GEC problem in the Ukrainian language.

我们的研究旨在应用 RedPenNet 方法解决乌克兰语的语法错误纠正问题，通过减少具体 Sequence-To-Edits 模型中的架构和参数的冗余，保持半自回归的优势，我们的模型在 BEA-2019（测试）上达到了 77.60 的 $F_{0.5}$ 分数，可以被视为最先进的，而在 UAGEC + 流利度（测试）基准上达到了 67.71 的分数。