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On the Use of N-gram Transducers for Dialogue Annotation. Spoken Dialogue Systems Technology and Design. Springer. 2011. Wolfgang Minker, Gary G. Lee, Satoshi Nakamura, Joseph Mariani (Editors). pp. 255-276.ON THE USE OF N-GRAM TRANSDUCERS FOR DIALOGUE ANNOTATION Vicent Tamarit, Carlos-D. Martíinez-Hinarejos, and José-Miguel Benedí The implementation of dialogue systems is one of the most interesting applications of language technologies. Statistical models can be used in this implementation, allowing for a more flexible approach than when using rules defined by a human expert. However, statistical models require large amounts of dialogues annotated with dialogue-function labels (usually Dialogue Acts), and the annotation process is hard and time-consuming. Consequently, the use of other statistical models to obtain faster annotations is really interesting for the development of dialogue systems. In this work we compare two statistical models for dialogue annotation, a more classical Hidden Markov Model (HMM) based model and the new N-gram Transducers (NGT) model. This comparison is performed on two corpora of different nature, the well-known SwitchBoard corpus and the DIHANA corpus. The results show that the NGT model produces a much more accurate annotation that the HMM-based model (even 11% less error in the SwitchBoard corpus).