Sequence-to-sequence models have shown strong performance across a broad
range of applications. However, their application to parsing and generating
text usingAbstract Meaning Representation (AMR)has been limited, due to the
relatively limited amount of labeled data and the non-sequential nature of the
AMR graphs. We present a novel training procedure that can lift this limitation
using millions of unlabeled sentences and careful preprocessing of the AMR
graphs. For AMR parsing, our model achieves competitive results of 62.1SMATCH,
the current best score reported without significant use of external semantic
resources. For AMR generation, our model establishes a new state-of-the-art
performance of BLEU 33.8. We present extensive ablative and qualitative
analysis including strong evidence that sequence-based AMR models are robust
against ordering variations of graph-to-sequence conversions.

使用数百万个未标记的句子和对 AMR 图的精心预处理，我们提出了一种新颖的训练过程，成功地解决了 AMR 中有限的标记数据和非顺序的图表征对序列到序列模型的限制。我们在 AMR 解析和生成任务上取得了竞争性结果，并得到了丰富的分析和证据表明序列基 AMR 模型对图表征的排序变化具有鲁棒性。