  Constituent and dependency parsing, the two classic forms of syntactic
parsing, have been found to benefit from joint training and decoding under a
uniform formalism, Head-driven Phrase Structure Grammar (HPSG). However,
decoding this unified grammar has a higher time complexity ($O(n^5)$) than
decoding either form individually ($O(n^3)$) since more factors have to be
considered during decoding. We thus propose an improved head scorer that helps
achieve a novel performance-preserved parser in $O$($n^3$) time complexity.
Furthermore, on the basis of this proposed practical HPSG parser, we
investigated the strengths of HPSG-based parsing and explored the general
method of training an HPSG-based parser from only a constituent or dependency
annotations in a multilingual scenario. We thus present a more effective, more
in-depth, and general work on HPSG parsing.
