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• The proposed method outputs a deduplicated set of STL candidate formulas with plausibility scores, rather than a single formula.method 95%
• The method preserves structural ambiguity (attachment and scope ambiguity) inherent in natural language during translation to STL.method 90%
• The translation pipeline is based on a three-stage process: n-best CCG parsing, STL-oriented template-based semantic composition, and canonicalization with score aggregation.method 90%
• The method generates multiple STL candidates for genuinely ambiguous inputs while collapsing unambiguous or canonically equivalent derivations to a single formula.result 85%
• Natural language provides a convenient interface for specifying STL tasks but its structural ambiguity makes one-to-one translation unreliable.technical 95%
• The semantic templates used in the composition stage are fixed, and ambiguity arises from how they are composed along the parse tree.technical 85%
• The canonicalization step maps derivationally different but equivalent outputs to a common normal form to prevent artificial inflation of the candidate set.method 85%
• Writing STL formulas directly is difficult for non-expert users, creating a need for natural language interfaces.market 90%

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