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We present a system to translate natural language sentences to formulas in aformal or a knowledge representation language. Our system uses two inverselambda-calculus operators and using them can take as input the semanticrepresentation of some words, phrases and sentences and from that derive thesemantic representation of other words and phrases. Our inverse lambda operatorworks on many formal languages including first order logic, database querylanguages and answer set programming. Our system uses a syntactic combinatorialcategorial parser to parse natural language sentences and also to construct thesemantic meaning of the sentences as directed by their parsing. The same parseris used for both. In addition to the inverse lambda-calculus operators, oursystem uses a notion of generalization to learn semantic representation ofwords from the semantic representation of other words that are of the samecategory. Together with this, we use an existing statistical learning approachto assign weights to deal with multiple meanings of words. Our system producesimproved results on standard corpora on natural language interfaces for robotcommand and control and database queries.

Using Inverse lambda and Generalization to Translate English to Formal Languages