Dialogue act tagging with Transformation-Based Learning
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
The CURRENT platform: building conversational agents in oz
MOZ'04 Proceedings of the Second international conference on Multiparadigm Programming in Mozart/Oz
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This paper presents the design and implementation of a simple and robust dialogue system front end which performs rule-driven, incremental processing of user contributions. We describe how a particular instantiation of the front end can be made to perform a variety of tasks such as part of speech disambiguation, word sense disambiguation, noun phrase detection, and dialogue act recognition, always in an incremental manner. Still, incrementality is never allowed to compromise the accuracy of the disambiguation decisions taken by the system. Incrementality is cautious; when correctness is at stake, decisions are delayed, until more information becomes available. Furthermore, the format of the necessary rules is very simple and uniform across different tasks, and rules can be learned automatically from tagged dialogue corpora, using transformation-based learning.