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An Extended GHKM Algorithm for Inducing λ -SCFG

An Extended GHKM Algorithm for Inducing λ -SCFG. Peng Li pengli09@gmail.com Tsinghua University. Semantic Parsing. Mapping natural language (NL) sentence to its computable meaning representation (MR). NL: Every boy likes a star. MR:. predicate. variable. Motivation.

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An Extended GHKM Algorithm for Inducing λ -SCFG

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  1. An Extended GHKM Algorithm forInducing λ-SCFG Peng Li pengli09@gmail.com Tsinghua University

  2. Semantic Parsing • Mapping natural language (NL) sentence to its computable meaning representation (MR) NL: Every boy likes a star MR: predicate variable

  3. Motivation • Common way: inducing probabilistic grammar PCFG: Probabilistic Context Free Grammar

  4. Motivation • Common way: inducing probabilistic grammar CCG: Combinatory Categorial Grammar

  5. Motivation • Common way: inducing probabilistic grammar SCFG: Synchronous Context Free Grammar

  6. 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

  7. Background • State of the art: SCFG + λ-calculus (λ-SCFG) • λ-calculus • λ-expression: • β-conversion: bound variable substitution • α-conversion: bound variable renaming

  8. λ-SCFG Rule Extraction • Outline • Building training examples • Transforming logical forms to trees • Aligning trees with sentences • Identifying frontier nodes • Extracting minimal rules • Extracting composed rules

  9. Building Training Examples NL: Every boy likes a star MR:

  10. Building Training Examples

  11. Building Training Examples

  12. Building Training Examples boy human pop like

  13. Building Training Examples boy human pop like Every boy likes a star

  14. Identifying Frontier Nodes

  15. Identifying Frontier Nodes

  16. Identifying Frontier Nodes

  17. Identifying Minimal Frontier Tree

  18. Identifying Minimal Frontier Tree

  19. Identifying Minimal Frontier Tree

  20. Identifying Minimal Frontier Tree

  21. Minimal Rule Extraction X X

  22. Minimal Rule Extraction X X

  23. Minimal Rule Extraction X X

  24. Composed Rule Extraction

  25. λ-SCFG Rule Extraction • Outline • Building training examples • Transforming logical forms to trees • Aligning trees with sentences • Identifying frontier nodes • Extracting minimal rules • Extracting composed rules

  26. Modeling • Log-linear model + MERT training • Target

  27. Experiments • Dataset: GEOQUERY • 880 English questions with corresponding Prolog logical form • Metric

  28. Experiments CCG PCFG SCFG

  29. Experiments • F-measure for different languages * en - English, ge - German, el - Greek, th - Thai

  30. Experiments

  31. Experiments

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