  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.
