Abstract
AMR-to-text generation is a problem recently introduced to the NLP community, in which the goal is to generate sentences from Abstract Meaning Representation (AMR) graphs. Sequence-to-sequence models can be used to this end by converting the AMR graphs to strings. Approaching the problem while working directly with graphs requires the use of graph-to-sequence models that encode the AMR graph into a vector representation. Such encoding has been shown to be beneficial in the past, and unlike sequential encoding, it allows us to explicitly capture reentrant structures in the AMR graphs. We investigate the extent to which reentrancies (nodes with multiple parents) have an impact on AMR-to-text generation by comparing graph encoders to tree encoders, where reentrancies are not preserved. We show that improvements in the treatment of reentrancies and long-range dependencies contribute to higher overall scores for graph encoders. Our best model achieves 24.40 BLEU on LDC2015E86, outperforming the state of the art by 1.1 points and 24.54 BLEU on LDC2017T10, outperforming the state of the art by 1.24 points.
Abstract (translated)
AMR到文本生成是最近被引入到NLP社区的一个问题，其目标是从抽象意义表示（AMR）图生成句子。序列到序列模型可以通过将AMR图转换为字符串来实现这一目的。在直接处理图形时，要解决这个问题，需要使用图形将AMR图形编码为矢量表示的模型排序。这种编码在过去被证明是有益的，与顺序编码不同，它允许我们显式地捕获AMR图中的可重入结构。通过比较图编码器和树编码器，我们研究了重入（具有多个父节点的节点）对AMR到文本生成的影响程度，其中不保留重入。我们表明，在处理重入度和长期依赖性方面的改进有助于提高图形编码器的总体得分。我们的最佳模型在ldc2015e86上达到24.40 bleu，在ldc2017t10上超过最先进的1.1点和24.54 bleu，超过最先进的1.24点。
URL
https://arxiv.org/abs/1903.11410
