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SETZER, M. HEPPLE & R. CATIZONE a somewhat more complicated n-gram method to the SWITCHBOARD corpus (which employs both n-gram language models of individual utterances, and ngram models over dialogue act sequences) and achieve a tagging accuracy of 71% on word transcripts, drawing on the full 205k utterances of the data. Of this, 198k utterances were used for training, with a 4k utterance test set. These performance differences can be seen to reflect the differential difficulty of tagging for the two corpora.
An Application of Reinforcement Learning to Dialogue Strategy Selection in a Spoken Dialogue System for Email”. Journal of Artificial Intelligence Research 12:387-416. A. 1970. “Transition Network Grammars for Natural Language Analysis”. 591-606. J. 2000. “Probabilistic Methods in Spoken Dialogue Systems”. 1389-1402. Learning Domain Theories S TEPHEN G. P ULMAN & M ARIA L IAKATA Computational Linguistics Group, Centre for Linguistics, Oxford University Abstract By a ‘domain theory’ we mean a collection of facts and generalisations or rules which capture what commonly happens (or does not happen) in some domain of interest.
2002. “Multi-Layered Dialogue Annotation for Automated Multilingual Customer Service”. Proceedings of the ISLE workshop on Dialogue Tagging for Multimodal Human Computer Interaction. Edinburgh. , T. Strzalkowski & M. Wu. 2003. “Dialogue Management for an Automated Multilingual Call Center”. Proceedings of the HLT-NAACL 2003 Workshop on Research Directions in Dialogue Processing, 10-12. Edmonton, Canada. Hearst, Marti A. 1993. “TextTiling: A Quantitative Approach to Discourse Segmentation”. Technical Report UCB:S2K-93-24.
A. V. Gulin (A tribute on the occasion of his 60th birthday)