Attention, intentions, and the structure of discourse
Computational Linguistics
ISSD-93 Selected papers presented at the international symposium on Spoken dialogue
Limited attention and discourse structure
Computational Linguistics
Transition network grammars for natural language analysis
Communications of the ACM
A pragmatics-based approach to ellipsis resolution
Computational Linguistics
A robust and efficient three-layered dialogue component for a speech-to-speech translation system
EACL '95 Proceedings of the seventh conference on European chapter of the Association for Computational Linguistics
Utilizing statistical dialogue act processing in VERBMOBIL
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Mixed initiative in dialogue: an investigation into discourse segmentation
ACL '90 Proceedings of the 28th annual meeting on Association for Computational Linguistics
JANUS: a speech-to-speech translation system using connectionist and symbolic processing strategies
ICASSP '91 Proceedings of the Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference
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In some cases, to make a proper translation of an utterance in a dialogue, different pieces of contextual information are needed. Interpreting such utterances often requires dialogue analysis including speech acts and discourse analysis. In this paper, a statistical dialogue analysis model for Korean–English dialogue machine translation based on speech acts is proposed. The model uses syntactic patterns and n-grams of speech acts. The syntactic patterns include surface syntactic features which are related to the language-dependent expressions of speech acts. Speech-act n-grams are used to approximate the context of utterances. The key feature is the use of speech-act n-grams based on hierarchical recency. Experimental results with trigrams show that the proposed model achieves an accuracy of 66.87% for the top candidate and 82.35% for the top three candidates. It indicates that the proposed model based on hierarchical recency outperforms the model based on linear recency.