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 …

… Integrated syntactic and semantic parser. Assumption: A training set consisting of natural
language and meaning representation pairs. … Using the World’s response to learn a
semantic parser. … A lightweight semantic parsing model that doesn’t require annotated data. …