Type-Driven Incremental Semantic Parsing with Polymorphism
This work addresses efficiency and representation limitations in semantic parsing for natural language processing applications, though it appears incremental in nature.
The authors tackled the problems of slow speed and primitive representation in semantic parsing by introducing a linear-time incremental algorithm that uses type-checking instead of syntactic grammar and enriches the type system with polymorphism. Their system achieved very accurate parses in GeoQuery, Jobs, and Atis domains.
Semantic parsing has made significant progress, but most current semantic parsers are extremely slow (CKY-based) and rather primitive in representation. We introduce three new techniques to tackle these problems. First, we design the first linear-time incremental shift-reduce-style semantic parsing algorithm which is more efficient than conventional cubic-time bottom-up semantic parsers. Second, our parser, being type-driven instead of syntax-driven, uses type-checking to decide the direction of reduction, which eliminates the need for a syntactic grammar such as CCG. Third, to fully exploit the power of type-driven semantic parsing beyond simple types (such as entities and truth values), we borrow from programming language theory the concepts of subtype polymorphism and parametric polymorphism to enrich the type system in order to better guide the parsing. Our system learns very accurate parses in GeoQuery, Jobs and Atis domains.