An Extended GHKM Algorithm for Inducing λ-SCFG Peng Li Tsinghua University
Semantic Parsing Mapping natural language (NL) sentence to its computable meaning representation (MR) NL: Every boy likes a star MR: variable predicate
Motivation Common way: inducing probabilistic grammar PCFG: Probabilistic Context Free Grammar
Motivation Common way: inducing probabilistic grammar CCG: Combinatory Categorial Grammar
Motivation Common way: inducing probabilistic grammar SCFG: Synchronous Context Free Grammar
Motivation State of the art: SCFG + λ-calculus (λ-SCFG) Major challenge: grammar induction – It is much harder to find the correspondence between NL sentence and MR than between NL sentences SCFG rule extraction is well-studied in MT GHKM is the most widely used algorithm We want to adapt GHKM to semantic parsing Experimental results show that we get the state- of-the-art performance
Background State of the art: SCFG + λ-calculus (λ-SCFG) λ-calculus – λ-expression: – β-conversion: bound variable substitution – α-conversion: bound variable renaming
λ-SCFG Rule Extraction Outline 1.Building training examples 1.Transforming logical forms to trees 2.Aligning trees with sentences 2.Identifying frontier nodes 3.Extracting minimal rules 4.Extracting composed rules
Building Training Examples NL: Every boy likes a star MR:
Building Training Examples
boy human pop like
Building Training Examples boy human pop like Every boy likes a star
Identifying Frontier Nodes
Identifying Minimal Frontier Tree
Minimal Rule Extraction X X
X X
X X
Composed Rule Extraction
λ-SCFG Rule Extraction Outline 1.Building training examples 1.Transforming logical forms to trees 2.Aligning trees with sentences 2.Identifying frontier nodes 3.Extracting minimal rules 4.Extracting composed rules
Modeling Log-linear model + MERT training Target
Parsing Type checking (Wong and Mooney, 2007)
Experiments Dataset: G EOQUERY – 880 English questions with corresponding Prolog logical form – Metric
Experiments SCFG PCFG CCG
Experiments F-measure for different languages * en - English, ge - German, el - Greek, th - Thai
Experiments
Experiments