Current approaches to semantic parsing, the task of converting text to a formal meaning representation, rely on annotated training data mapping sentences to logi- cal forms. Providing this supervision is a major bottleneck in scaling semantic parsers. This paper presents a new learn- ing paradigm aimed at alleviating the su- pervision burden. We develop two novel learning algorithms capable of predicting complex structures which only rely on a binary feedback signal based on the con- text 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 us- ing any annotated meaning representations learning with a weak feedback signal is ca- pable of producing a parser that is compet- itive with fully supervised parsers.