Higher-order methods for dependency parsing can partially but not fully
address the issue that edges in dependency trees should be constructed at the
text span/subtree level rather than word level. In this paper, we propose a new
method for dependency parsing to address this issue. The proposed method
constructs dependency trees by directly modeling span-span (in other words,
subtree-subtree) relations. It consists of two modules: the {\it text span
proposal module} which proposes candidate text spans, each of which represents
a subtree in the dependency tree denoted by (root, start, end); and the {\it
span linking module}, which constructs links between proposed spans. We use the
machine reading comprehension (MRC) framework as the backbone to formalize the
span linking module, where one span is used as a query to extract the text
span/subtree it should be linked to. The proposed method has the following
merits: (1) it addresses the fundamental problem that edges in a dependency
tree should be constructed between subtrees; (2) the MRC framework allows the
method to retrieve missing spans in the span proposal stage, which leads to
higher recall for eligible spans. Extensive experiments on the PTB, CTB and
Universal Dependencies (UD) benchmarks demonstrate the effectiveness of the
proposed method. The code is available at
https://github.com/ShannonAI/mrc-for-dependency-parsing

本文提出了一种新的依存句法分析方法，旨在通过模拟子树与子树之间的关系，解决依存树中边的构造应该在文本跨度 / 子树级别而不是词级别上的问题。