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Develop a fast semantic decoder Robust to speech recognition noise Trainable on different domains: Tourist information

Transformation-based Learning for Semantic Parsing. F. Jurcicek, M . Gasic , S . Keizer, F. Mairesse, B. Thomson, K . Yu, S. Young Cambridge University Engineering Department | {fj228, mg436 , sk561 , f.mairesse, brmt2, ky219, sjy}@ eng.cam.ac.uk. Goals. Evaluation. Example of parsing.

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Develop a fast semantic decoder Robust to speech recognition noise Trainable on different domains: Tourist information

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  1. Transformation-based Learning for Semantic Parsing F. Jurcicek, M. Gasic, S. Keizer, F. Mairesse, B. Thomson, K. Yu, S. Young Cambridge University Engineering Department | {fj228, mg436, sk561, f.mairesse, brmt2, ky219, sjy}@eng.cam.ac.uk Goals Evaluation Example of parsing • Develop a fast semantic decoder • Robust to speech recognition noise • Trainable on different domains: • Tourist information (TownInfo) • Air travel information system (ATIS) • Semantic parsing maps natural language to formal langage – most of the maping incurrent dialogue is fairly simple(cities, times, ... ). • To develop a fast semantic parser, we have to take advantage of this feature. • Inference of compact set of transforation rules which transforms the initial naive semantic hypothesis. • Example: • find all the flights between Toronto and Sandiego that arrive on Saturday • Parser assigns to inital semantics to input sentece. • Rules, whose triggers match the sentence a the hypothesised semantics, are sequentially applied. • Resulting semantics • Compare semantic tuple classifiers with: • A handcrafted Phoenix grammar • The Hidden Vector State model (He & Young, 2006) • Probabilistic Combinatory Categorial Grammar Induction (Zettlemoyer & Collins, 2007) • Markov Logic networks (Meza-Ruiz et al., 2008) • Evaluation metrics: • Dialogue act type accuracy (e.g. inform or request) • Precision, recall and F-measure of dialogue act items (e.g. food=Chinese,toloc.city=New York) • Conclusion: semantic tuple classifiersare robust to noise and competitive with the state of the art – they provide a simple yet efficient solution to the spoken language understanding problem! • The code is available at http://code.google.com/p/tbed-parser . what are the lowest airfare from Washington DC to Boston GOAL = airfare airfare.type = lowest from.city = Wahington from.state = DC to.city = Boston GOAL= flight # trigger transformation „between toronto“ add the slot „from.city=Toronto“ „and sandiago“ add the slot „to.city=Sand Diaego“ „Saturday“ add the slot „departure.day=Saturday“ Motivation GOAL= flight from.city = Toronto to.city = Sandiego departure.day = Saturday Tranformation parsing Open source Triggers Transformations „tickets“ replace the goal by „airfare“ „flights * from“ & GOAL=airfare replace the goal by flight „Seatle“ add the slot „to.city=Seatle“ „connecting“replace the slot „to.city=*“ by „stop.city=*“ Acknowledgements This research was partly funded by the UK EPSRC under grant agreement EP/F013930/1 and by the EU FP7 Programme under grant agreement 216594 (CLASSiCproject: www.classic-project.org).

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