A maximum entropy approach to natural language processing
Computational Linguistics
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Prosody-based automatic segmentation of speech into sentences and topics
Speech Communication - Special issue on accessing information in spoken audio
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
Supertagging: an approach to almost parsing
Computational Linguistics
A uniform method of grammar extraction and its applications
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
Learning the structure of task-driven human-human dialogs
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Incorporating speaker and discourse features into speech summarization
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of 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
Enriching spoken language translation with dialog acts
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
Enriching speech recognition with automatic detection of sentence boundaries and disfluencies
IEEE Transactions on Audio, Speech, and Language Processing
Classification of feedback expressions in multimodal data
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
Classifying dialogue acts in one-on-one live chats
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Dialogue act modeling in a complex task-oriented domain
SIGDIAL '10 Proceedings of the 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue
N-best rescoring based on pitch-accent patterns
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
An affect-enriched dialogue act classification model for task-oriented dialogue
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Dialogue act recognition using reweighted speaker adaptation
SIGDIAL '12 Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Combining verbal and nonverbal features to overcome the 'information gap' in task-oriented dialogue
SIGDIAL '12 Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Enriching machine-mediated speech-to-speech translation using contextual information
Computer Speech and Language
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Prosody is an important cue for identifying dialog acts. In this paper, we show that modeling the sequence of acoustic-prosodic values as n-gram features with a maximum entropy model for dialog act (DA) tagging can perform better than conventional approaches that use coarse representation of the prosodic contour through summative statistics of the prosodic contour. The proposed scheme for exploiting prosody results in an absolute improvement of 8.7% over the use of most other widely used representations of acoustic correlates of prosody. The proposed scheme is discriminative and exploits context in the form of lexical, syntactic and prosodic cues from preceding discourse segments. Such a decoding scheme facilitates online DA tagging and offers robustness in the decoding process, unlike greedy decoding schemes that can potentially propagate errors. Our approach is different from traditional DA systems that use the entire conversation for offline dialog act decoding with the aid of a discourse model. In contrast, we use only static features and approximate the previous dialog act tags in terms of lexical, syntactic and prosodic information extracted from previous utterances. Experiments on the Switchboard-DAMSL corpus, using only lexical, syntactic and prosodic cues from three previous utterances, yield a DA tagging accuracy of 72% compared to the best case scenario with accurate knowledge of previous DA tags (oracle), which results in 74% accuracy.