Re-Ranking Models for Spoken Language Understanding

Proceedings of the 12th Conference of the European Chapter of the ACL, pages 202–210, Athens, Greece, 30 March – 3 April 2009. c⃝2009 Association for Computational Linguistics Re-Ranking Models For Spoken Language Understanding Marco Dinarelli University of Trento Italy dinarelli@disi.unitn.it Alessandro Moschitti University of Trento Italy moschitti@disi.unitn.it Giuseppe Riccardi University of Trento Italy riccardi@disi.unitn.it Abstract Spoken Language Understanding aims at mapping a natural language spoken sen- tence into a semantic representation. In the last decade two main approaches have been pursued: native models.generative and discrimi- The former is more ro- bust to overfitting whereas the latter is more robust to many irrelevant features. Additionally, the way in which these ap- proaches encode prior knowledge is very different and their relative performance changes based on the task. In this pa- per we describe a machine learning frame- work where both models are used: a gen- erative model produces a list of ranked hy- potheses whereas a discriminative model based on structure kernels and Support Vector Machines, re-ranks such list. We tested our approach on the MEDIA cor- pus (human-machine dialogs) and on a new corpus (human-machine and human- human dialogs) produced in the Euro- pean LUNA project. The results show a large improvement on the state-of-the-art in concept segmentation and labeling. 1 Introduction In Spoken Dialog Systems, the Language Under- standing module performs the task of translating a spoken sentence into its meaning representation based on semantic constituents. These are the units for meaning representation and are often re- ferred to as concepts. Concepts are instantiated by sequences of words, therefore a Spoken Language Understanding (SLU) module finds the association between words and concepts. In the last decade two major approaches have been proposed to find this correlation: (i) gener- ative models, whose parameters refer to the joint probability of concepts and constituents; and (ii) discriminative models, which learn a classifica- tion function to map words into concepts based on geometric and statistical properties. An ex- ample of generative model is the Hidden Vector State model (HVS) (He and Young, 2005). This approach extends the discrete Markov model en- coding the context of each state as a vector. State transitions are performed as stack shift operations followed by a push of a preterminal semantic cat- egory label. In this way the model can capture se- mantic hierarchical structures without the use of tree-structured data. Another simpler but effec- tive generative model is the one based on Finite State Transducers. It performs SLU as a transla- tion process from words to concepts using Finite State Transducers (FST). An example of discrim- inative model used for SLU is the one based on Support Vector Machines (SVMs) (Vapnik, 1995), as shown in (Raymond and Riccardi, 2007). In this approach, data are mapped into a vector space and SLU is performed as a classification problem using Maximal Margin Classifiers (Shawe-Taylor and Cristianini, 2004). Generative models have the advantage to be more robust to overfitting on training data, while discriminative models are more robust to irrele- …

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