Both syntactic and semantic structures are key linguistic contextual clues,
in which parsing the latter has been well shown beneficial from parsing the
former. However, few works ever made an attempt to let semantic parsing help
syntactic parsing. As linguistic representation formalisms, both syntax and
semantics may be represented in either span (constituent/phrase) or dependency,
on both of which joint learning was also seldom explored. In this paper, we
propose a novel joint model of syntactic and semantic parsing on both span and
dependency representations, which incorporates syntactic information
effectively in the encoder of neural network and benefits from two
representation formalisms in a uniform way. The experiments show that semantics
and syntax can benefit each other by optimizing joint objectives. Our single
model achieves new state-of-the-art or competitive results on both span and
dependency semantic parsing on Propbank benchmarks and both dependency and
constituent syntactic parsing on Penn Treebank.

本研究提出了一种新的基于神经网络的两种表征形式（span 和 dependency）的句法和语义联合模型，该模型有效地整合了句法信息，最大化优化联合目标，试验结果表明语义和句法可以相互促进，以全新的表现水平取得了 Propbank 基准测试集上的最好成绩。