论文标题
诱导和使用对准基于过渡的AMR解析
Inducing and Using Alignments for Transition-based AMR Parsing
论文作者
论文摘要
基于过渡的解析器用于抽象含义表示（AMR）依赖于节点到词对齐。这些对齐方式是从解析器培训中分别学习的，需要复杂的基于规则的组件，预处理和后处理，以满足特定领域的约束。解析器还训练了对齐管道的点幅度，从而忽略了由于对齐的固有歧义而导致的不确定性。在这项工作中，我们探索了克服这些局限性的两种途径。首先，我们为AMR提出了一个神经对准器，该神经对准器在不依赖复杂管道的情况下学习节点到词对齐。随后，我们通过考虑由对准器不确定性产生的Oracle动作序列进行分布来探索对准器和解析器训练的更严格整合。经验结果表明，从AMR2.0到AMR3.0 Corpora，这种方法会导致更准确的一致性和概括。我们获得了一个新的纯金型模型的最新技术，与经过银色训练的性能相匹配，而无需在AMR3.0上进行光束搜索。
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.
