Driving Semantic Parsing from World's Response

James Clarke     Dan Goldwasser     Ming-Wei Chang     Dan Roth.    
Computational Natural Language Learning (CoNLL), 2010


Current approaches to semantic parsing, the task of converting text to a formal meaning representation, rely on annotated training data mapping sentences to logical forms. Providing this supervision is a major bottleneck in scaling semantic parsers. This paper presents a new learning paradigm aimed at alleviating the supervision burden. We develop two novel learning algorithms capable of predicting complex structures which only rely on a binary feedback signal based on the context of an external world. In addition we reformulate the semantic parsing problem to reduce the dependency of the model on syntactic patterns, thus allowing our parser to scale better using less supervision. Our results surprisingly show that without using any annotated meaning representations learning with a weak feedback signal is capable of producing a parser that is competitive with fully supervised parsers.

Bib Entry

    author = "James Clarke and Dan Goldwasser and Ming-Wei Chang and Dan Roth.",
    title = "Driving Semantic Parsing from World's Response",
    booktitle = "Computational Natural Language Learning (CoNLL)",
    year = "2010"