An operator predictor constrains the logical-form search space, increasing search success and reducing spurious logical forms, which improves semantic parser training and lifts CSQA KBQA accuracy from 67% to 72%.
Inferring Logical Forms From Denotations
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abstract
A core problem in learning semantic parsers from denotations is picking out consistent logical forms--those that yield the correct denotation--from a combinatorially large space. To control the search space, previous work relied on restricted set of rules, which limits expressivity. In this paper, we consider a much more expressive class of logical forms, and show how to use dynamic programming to efficiently represent the complete set of consistent logical forms. Expressivity also introduces many more spurious logical forms which are consistent with the correct denotation but do not represent the meaning of the utterance. To address this, we generate fictitious worlds and use crowdsourced denotations on these worlds to filter out spurious logical forms. On the WikiTableQuestions dataset, we increase the coverage of answerable questions from 53.5% to 76%, and the additional crowdsourced supervision lets us rule out 92.1% of spurious logical forms.
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cs.CL 1years
2019 1verdicts
CONDITIONAL 1representative citing papers
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Effective Search of Logical Forms for Weakly Supervised Knowledge-Based Question Answering
An operator predictor constrains the logical-form search space, increasing search success and reducing spurious logical forms, which improves semantic parser training and lifts CSQA KBQA accuracy from 67% to 72%.