Learning a semantic parser from spoken utterances

Semantic parsers map natural language input into semantic representations. In this paper, we present an approach that learns a semantic parser in the form of a lexicon and an inventory of syntactic patterns from ambiguous training data which is applicable to spoken utterances. We only assume the availability of a task-independent phoneme recognizer, making it easy to adapt to other tasks and yielding no a priori restriction concerning the vocabulary that the parser can process. In spite of these low requirements, we show that our approach can be successfully applied to both spoken and written data.