Improving Semantic parsing with enriched synchronous context-free grammar

Abstract

Semantic parsing maps a sentence in natural language into a structured meaning representation. Previous studies show that semantic parsing with synchronous contextfree grammars (SCFGs) achieves favorable performance over most other alternatives. Motivated by the observation that the performance of semantic parsing with SCFGs is closely tied to the translation rules, this paper explores extending translation rules with high quality and increased coverage in three ways. First, we introduce structure informed non-terminals, better guiding the parsing in favor of well formed structure, instead of using a uninformed non-terminal in SCFGs. Second, we examine the difference between word alignments for semantic parsing and statistical machine translation (SMT) to better adapt word alignment in SMT to semantic parsing. Finally, we address the unknown word translation issue via synthetic translation rules. Evaluation on the standard GeoQuery benchmark dataset shows that our approach achieves the state-of-the-art across various languages, including English, German and Greek.