Attention, intentions, and the structure of discourse
Computational Linguistics
Dialogue act modeling for automatic tagging and recognition of conversational speech
Computational Linguistics
Empirical studies on the disambiguation of cue phrases
Computational Linguistics
The reliability of a dialogue structure coding scheme
Computational Linguistics
Dialogue act tagging with Transformation-Based Learning
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Training a Dialogue Act Tagger for human-human and human-computer travel dialogues
SIGDIAL '02 Proceedings of the 3rd SIGdial workshop on Discourse and dialogue - Volume 2
FLSA: extending latent semantic analysis with features for dialogue act classification
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
HMM and neural network based speech act detection
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 01
Domain adaptation with unlabeled data for dialog act tagging
DANLP 2010 Proceedings of the 2010 Workshop on Domain Adaptation for Natural Language Processing
Using syntactic and semantic based relations for dialogue act recognition
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Automatic extraction of cue phrases for cross-corpus dialogue act classification
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Using a slim function word classifier to recognise instruction dialogue acts
KES-AMSTA'11 Proceedings of the 5th KES international conference on Agent and multi-agent systems: technologies and applications
SIGDIAL '11 Proceedings of the SIGDIAL 2011 Conference
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We present recent work in the area of Cross-Domain Dialogue Act tagging. Our experiments investigate the use of a simple dialogue act classifier based on purely intra-utterance features - principally involving word n-gram cue phrases. We apply automatically extracted cues from one corpus to a new annotated data set, to determine the portability and generality of the cues we learn. We show that our automatically acquired cues are general enough to serve as a cross-domain classification mechanism.