Abstract
The design rationale guiding the development of the reductionist dialog act based translation module in Verbmobil was robustness. Even in case the speech recognition or the prosodic processing does not perform perfectly, this module extracts and translates the main intentions and facts related to the domain. In a three step approach, first the dialog act describing the intention is computed using a statistical approach. The second step is the construction of the propositional content with robust hierarchical finite state transducers. For the definition of the transducers, knowledge sources available in Verbmobil are exploited. The resulting representation of these two steps is used in a template based finite state generator to realize the target language expressions. The internal representation is also communicated to the dialog module where it plays an important part in maintaining the dialog state.
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© 2000 Springer-Verlag Berlin Heidelberg
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Reithinger, N., Engel, R. (2000). Robust Content Extraction for Translation and Dialog Processing. In: Wahlster, W. (eds) Verbmobil: Foundations of Speech-to-Speech Translation. Artificial Intelligence. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-04230-4_31
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DOI: https://doi.org/10.1007/978-3-662-04230-4_31
Publisher Name: Springer, Berlin, Heidelberg
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