10th Annual Conference of the International Speech Communication Association

Brighton, United Kingdom
September 6-10, 2009

Transformation-Based Learning for Semantic Parsing

F. Jurčíček, M. Gašić, S. Keizer, F. Mairesse, B. Thomson, K. Yu, S. Young

University of Cambridge, UK

This paper presents a semantic parser that transforms an initial semantic hypothesis into the correct semantics by applying an ordered list of transformation rules. These rules are learnt automatically from a training corpus with no prior linguistic knowledge and no alignment between words and semantic concepts. The learning algorithm produces a compact set of rules which enables the parser to be very efficient while retaining high accuracy. We show that this parser is competitive with respect to the state-ofthe- art semantic parsers on the ATIS and TownInfo tasks.

Full Paper

Bibliographic reference.  Jurčíček, F. / Gašić, M. / Keizer, S. / Mairesse, F. / Thomson, B. / Yu, K. / Young, S. (2009): "Transformation-based learning for semantic parsing", In INTERSPEECH-2009, 2719-2722.