Learning Dialog Act Processing

In this paper we describe a new approach for learning dialog act processing. In this approach we integrate a symbolic semantic segmentation parser with a learning dialog act network. In order to support the unforeseeable errors and variations of spoken language we have concentrated on robust data-driven learning. This approach already compares favorably with the statistical average plausibility method, produces a segmentation and dialog act assignment for all utterances in a robust manner, and reduces knowledge engineering since it can be bootstrapped from rather small corpora. Therefore, we consider this new approach as very promising for learning dialog act processing.

@InProceedings{WL96,
 	 author =  {Wermter, Stefan and Löchel, Matthias},
 	 title = {Learning Dialog Act Processing},
 	 booktitle = {Proceedings of the International Conference on Computational Linguistics},
 	 journal = {None},
 	 editors = {}
 	 number = {}
 	 volume = {}
 	 pages = {740--745},
 	 year = {1996},
 	 month = {}
 	 publisher = {None},
 	 doi = {10.3115/993268.993297},
 }