Improving Semantic Parsing with Enriched Synchronous Context-Free

Semantic parsing maps a sentence in natu-ral language into a structured meaning rep-resentation. Previous studies show that se-mantic parsing with synchronous context-free grammars (SCFGs) achieves favor-able performance over most other alter-natives. Motivated by the observation that the performance of semantic pars-ing with SCFGs is closely tied to the translation rules, this paper explores ex-tending translation rules with high qual-ity 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 differ-ence between word alignments for seman-tic parsing and statistical machine transla-tion (SMT) to better adapt word alignment in SMT to semantic parsing. Finally, we address the unknown word translation is-sue via synthetic translation rules. Eval-uation on the standard GeoQuery bench-mark dataset shows that our approach achieves the state-of-the-art across various languages, including English, German and Greek.