论文标题
严格的广度优先解析
Strictly Breadth-First AMR Parsing
论文作者
论文摘要
AMR解析是将句子映射到AMR语义图的任务。我们专注于这项任务的广度优先策略，该策略最近提出了，并且比其他策略取得了更好的性能。但是，此策略下的当前模型仅\ emph {鼓励}模型以广度优先的阶段生成AMR图，但是\ emph {无法保证}。为了解决这个问题，我们提出了一种新的体系结构，该架构\ emph {确保}，解析将严格遵循广度优先。在每个解析步骤中，我们介绍了\ textbf {focused parent}顶点，并使用此顶点指导生成。借助这种新的体系结构以及句子和图形编码器的其他一些改进，我们的模型在AMR 1.0和2.0数据集上都能获得更好的性能。
AMR parsing is the task that maps a sentence to an AMR semantic graph automatically. We focus on the breadth-first strategy of this task, which was proposed recently and achieved better performance than other strategies. However, current models under this strategy only \emph{encourage} the model to produce the AMR graph in breadth-first order, but \emph{cannot guarantee} this. To solve this problem, we propose a new architecture that \emph{guarantees} that the parsing will strictly follow the breadth-first order. In each parsing step, we introduce a \textbf{focused parent} vertex and use this vertex to guide the generation. With the help of this new architecture and some other improvements in the sentence and graph encoder, our model obtains better performance on both the AMR 1.0 and 2.0 dataset.
