Abstract
Semantic parsing considers the task of transducing natural language (NL) utterances into
machine executable meaning representations
(MRs). While neural network-based semantic parsers have achieved impressive improvements over previous methods, results are still
far from perfect, and cursory manual inspection can easily identify obvious problems such
as lack of adequacy or coherence of the generated MRs. This paper presents a simple
approach to quickly iterate and improve the
performance of an existing neural semantic
parser by reranking an n-best list of predicted
MRs, using features that are designed to fix
observed problems with baseline models. We
implement our reranker in a competitive neural semantic parser and test on four semantic
parsing (GEO, ATIS) and Python code generation (DJANGO, CONALA) tasks, improving the strong baseline parser by up to 5.7%
absolute in BLEU (CONALA) and 2.9% in
accuracy (DJANGO), outperforming the best
published neural parser results on all four
datasets.1