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.
Cite as: Jurčíček, F., Gašić, M., Keizer, S., Mairesse, F., Thomson, B., Yu, K., Young, S. (2009) Transformation-based learning for semantic parsing. Proc. Interspeech 2009, 2719-2722, doi: 10.21437/Interspeech.2009-695
@inproceedings{jurcicek09_interspeech,
author={F. Jurčíček and M. Gašić and S. Keizer and F. Mairesse and B. Thomson and K. Yu and S. Young},
title={{Transformation-based learning for semantic parsing}},
year=2009,
booktitle={Proc. Interspeech 2009},
pages={2719--2722},
doi={10.21437/Interspeech.2009-695}
}