Spoken dialogue technology: enabling the conversational user interface
ACM Computing Surveys (CSUR)
Toward conversational human-computer interaction
AI Magazine
Automatic labeling of semantic roles
Computational Linguistics
Dialogue act modeling for automatic tagging and recognition of conversational speech
Computational Linguistics
PARADISE: a framework for evaluating spoken dialogue agents
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Evaluating discourse understanding in spoken dialogue systems
ACM Transactions on Speech and Language Processing (TSLP)
DATE: a dialogue act tagging scheme for evaluation of spoken dialogue systems
HLT '01 Proceedings of the first international conference on Human language technology research
Target word detection and semantic role chunking using support vector machines
NAACL-Short '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume of the Proceedings of HLT-NAACL 2003--short papers - Volume 2
Unsupervised learning of dependency structure for language modeling
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Japanese dependency structure analysis based on support vector machines
EMNLP '00 Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13
Hownet And the Computation of Meaning
Hownet And the Computation of Meaning
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This investigation proposes an approach to modeling the discourse of spoken dialogue using semantic dependency graphs. By characterizing the discourse as a sequence of speech acts, discourse modeling becomes the identification of the speech act sequence. A statistical approach is adopted to model the relations between words in the user's utterance using the semantic dependency graphs. Dependency relation between the headword and other words in a sentence is detected using the semantic dependency grammar. In order to evaluate the proposed method, a dialogue system for medical service is developed. Experimental results show that the rates for speech act detection and task-completion are 95.6% and 85.24%, respectively, and the average number of turns of each dialogue is 8.3. Compared with the Bayes' classifier and the Partial-Pattern Tree based approaches, we obtain 14.9% and 12.47% improvements in accuracy for speech act identification, respectively.