4,502 research outputs found
A Robust and Efficient Three-Layered Dialogue Component for a Speech-to-Speech Translation System
We present the dialogue component of the speech-to-speech translation system
VERBMOBIL. In contrast to conventional dialogue systems it mediates the
dialogue while processing maximally 50% of the dialogue in depth. Special
requirements like robustness and efficiency lead to a 3-layered hybrid
architecture for the dialogue module, using statistics, an automaton and a
planner. A dialogue memory is constructed incrementally.Comment: Postscript file, compressed and uuencoded, 15 pages, to appear in
Proceedings of EACL-95, Dublin
Some experiments in speech act prediction
In this paper, we present a statistical approach for speech act prediction in the dialogue component of the speech-to-speech translation system Verbmobil. The prediction algorithm is based on work known from language modelling and uses N-gram information computed from a training corpus. We demonstrate the performance of this method with 10 experiments. These experiments vary in two dimensions, namely whether the N-gram information is updated while processing, and whether deviations from the standard dialogue structure are processed. Six of the experiments use complete dialogues, while four process only the speech acts of one dialogue partner. It is shown that the predictions are best when using the update feature and deviations are not processed. Even the processing of incomplete dialogues then yields acceptable results. Another experiment shows that a training corpus size of about 40 dialogues is sufficient for the prediction task, and that the structure of the dialogues of the Verbmobil corpus we use differs remarkably with respect to the predictions
Some ideas for the automatic acquisition of dialogue structure
We are reporting on some initial results on the automatic acquisition of plan operators for a plan recognizer. The operators are derived from the Verbmobil corpus of negotiation dialogues hand-annotated with dialogue acts. The corpus is pre-classified and a set of plan operators is derived for every class. The plan operators are then tested on a set of unseen data. We also show some initial results
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