Transition-based parsers for Abstract Meaning Representation (AMR) rely on
node-to-word alignments. These alignments are learned separately from parser
training and require a complex pipeline of rule-based components,
pre-processing, and post-processing to satisfy domain-specific constraints.
Parsers also train on a point-estimate of the alignment pipeline, neglecting
the uncertainty due to the inherent ambiguity of alignment. In this work we
explore two avenues for overcoming these limitations. First, we propose a
neural aligner for AMR that learns node-to-word alignments without relying on
complex pipelines. We subsequently explore a tighter integration of aligner and
parser training by considering a distribution over oracle action sequences
arising from aligner uncertainty. Empirical results show this approach leads to
more accurate alignments and generalization better from the AMR2.0 to AMR3.0
corpora. We attain a new state-of-the art for gold-only trained models,
matching silver-trained performance without the need for beam search on AMR3.0.

通过直接学习 node-to-word alignments，提出了一个基于神经网络的 AMR 分析算法，并通过分析 aligner 有关的重要操作序列的分布集成于解析器的训练过程中，从而提高了对 AMR3.0 的性能。

基于转移的 AMR 分析的对齐生成及应用

Inducing and Using Alignments for Transition-based AMR Parsing

Transition-based parsers implemented with Pointer Networks have become the
new state of the art in dependency parsing, excelling in producing labelled
syntactic trees and outperforming graph-based models in this task. In order to
further test the capabilities of these powerful neural networks on a harder NLP
problem, we propose a transition system that, thanks to Pointer Networks, can
straightforwardly produce labelled directed acyclic graphs and perform semantic
dependency parsing. In addition, we enhance our approach with deep
contextualized word embeddings extracted from BERT. The resulting system not
only outperforms all existing transition-based models, but also matches the
best fully-supervised accuracy to date on the SemEval 2015 Task 18 English
datasets among previous state-of-the-art graph-based parsers.

通过使用 pointer networks 实现的转移句法分析器在依赖句法分析方面已经成为最新的技术水平，我们提出了一种基于指针网络的转移系统，可以直接生成标记有方向无环图并实现语义依赖分析。同时，我们使用从 BERT 提取的深层上下文化的词嵌入来增强我们的方法。这一结果不仅超越了所有现有的基于转移的模型，而且还匹配了 SemEval 2015 任务 18 英语数据集上先前最先进的基于图形的解析器的最佳完全监督准确率。