We present the application of statistical language modeling methods for the prediction of the next dialogue act. This prediction is used by different modules of the speech-to-speech translation system VERBMOBIL. The statistical approach uses deleted interpolation of n-gram frequencies as basis and determines the interpolation weights by a modified version of the standard optimization algorithm. Additionally, we present and evaluate different approaches to improve the prediction process, e.g. including knowledge from a dialogue grammar. Evaluation shows that including the speaker information and mirroring the data delivers the best results.
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