Recent years have witnessed the impressive progress in Neural Dependency Parsing. According to the different factorization approaches to the graph joint probabilities, existing parsers can be roughly divided into autoregressive and non-autoregressive patterns. The former means that the graph should be factorized into multiple sequentially dependent components, then it can be built up component by component. And the latter assumes these components to be independent so that they can be outputted in a one-shot manner. However, when treating the directed edge as an explicit dependency relationship, we discover that there is a mixture of independent and interdependent components in the dependency graph, signifying that both aforementioned models fail to precisely capture the explicit dependencies among nodes and edges. Based on this property, we design a Semi-Autoregressive Dependency Parser to generate dependency graphs via adding node groups and edge groups autoregressively while pouring out all group elements in parallel. The model gains a trade-off between non-autoregression and autoregression, which respectively suffer from the lack of target inter-dependencies and the uncertainty of graph generation orders. The experiments show the proposed parser outperforms strong baselines on Enhanced Universal Dependencies of multiple languages, especially achieving $4\%$ average promotion at graph-level accuracy. Also, the performances of model variations show the importance of specific parts.

翻译：近年来，神经依存解析取得了令人瞩目的进展。根据图联合概率的不同分解方法，现有解析器大致可分为自回归和非自回归模式。前者意味着图应被分解为多个顺序相关的组件，然后可以逐组件构建；后者则假设这些组件相互独立，从而可以一次性输出。然而，当将有向边视为显式依赖关系时，我们发现依存图中存在独立组件和相互依赖组件的混合，这表明上述两种模型都无法精确捕捉节点和边之间的显式依赖性。基于这一特性，我们设计了一种半自回归依存解析器，通过自回归方式添加节点组和边组，同时并行输出所有组内元素。该模型在非自回归和自回归之间取得了平衡，前者缺乏目标间相互依赖性，后者则面临图生成顺序的不确定性。实验表明，所提出的解析器在多种语言的增强型通用依存关系上优于强基线模型，尤其在图上准确率方面平均提升了4%。此外，模型变体的性能表现也突显了特定部分的重要性。