Morpho-syntactic Lexical Generalization for CCG Semantic Parsing.
Abstract
In this paper, we demonstrate that significant performance gains can be
achieved in CCG semantic parsing by introducing a linguistically motivated
grammar induction scheme. We present a new morpho-syntactic factored
lexicon that models systematic variations in morphology, syntax, and
semantics across word classes. The grammar uses domain-independent
facts about the English language to restrict the number of incorrect parses
that must be considered, thereby enabling effective learning from less
data. Experiments in benchmark domains match previous models with
one quarter of the data and provide new state-of-the-art results with all
available data, including up to 45% relative test-error reduction.