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.
Highlights
Semantic parsing, the task of mapping natural language (NL) sentences into a formal meaning representation language (MRL), has recently received a significant amount of attention with various models proposed over the past few years
NL’: what be the area of sea0le MRL’: answer@1 area_1@1 cityid@2 sea0le@s _@0 (b) aGer pre-‐processing naturally viewed as a statistical machine translation (SMT) task, which translates a sentence in NL into its meaning representation in MRL
It is intrinsically asymmetric: within the semantic formalism used in this paper, NL is often longer than MRL, and commonly contains words which have no counterpart in MRL
Summary
The task of mapping natural language (NL) sentences into a formal meaning representation language (MRL), has recently received a significant amount of attention with various models proposed over the past few years. Many attempts have been made to directly apply statistical machine translation (SMT) systems (or methodologies) to semantic parsing (Papineni et al, 1997; Macherey et al, 2001; Wong and Mooney, 2006; Andreas et al, 2013). Recent studies (Wong and Mooney, 2006; Andreas et al, 2013) show that semantic parsing with SCFGs, which form the basis of most existing statistical syntax-based translation models (Yamada and Knight, 2001; Chiang, 2007), achieves favorable results, this approach is still behind the most recent state-of-the-art. Evaluation on GeoQuery benchmark dataset shows that our approach obtains consistent improvement and achieves the state-of-the-art across various languages, including English, German and Greek
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