An efficient probabilistic context-free parsing algorithm that computes prefix probabilities
Computational Linguistics
An architecture for more realistic conversational systems
Proceedings of the 6th international conference on Intelligent user interfaces
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Robust probabilistic predictive syntactic processing: motivations, models, and applications
Robust probabilistic predictive syntactic processing: motivations, models, and applications
Incremental interpretation: applications, theory, and relationship to dynamic semantics
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 2
Expanding the scope of the ATIS task: the ATIS-3 corpus
HLT '94 Proceedings of the workshop on Human Language Technology
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Spoken language understanding from unaligned data using discriminative classification models
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
A general, abstract model of incremental dialogue processing
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Incremental dialogue processing in a micro-domain
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Towards natural language understanding of partial speech recognition results in dialogue systems
NAACL-Short '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
RUBISC: a robust unification-based incremental semantic chunker
SRSL '09 Proceedings of the 2nd Workshop on Semantic Representation of Spoken Language
SIGDIAL '09 Proceedings of the SIGDIAL 2009 Conference: The 10th Annual Meeting of the Special Interest Group on Discourse and Dialogue
SIGDIAL '09 Proceedings of the SIGDIAL 2009 Conference: The 10th Annual Meeting of the Special Interest Group on Discourse and Dialogue
SIGDIAL '11 Proceedings of the SIGDIAL 2011 Conference
Towards incremental speech generation in conversational systems
Computer Speech and Language
SDCTD '12 NAACL-HLT Workshop on Future Directions and Needs in the Spoken Dialog Community: Tools and Data
Markov logic networks for situated incremental natural language understanding
SIGDIAL '12 Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Incremental dialogue understanding and feedback for multiparty, multimodal conversation
IVA'12 Proceedings of the 12th international conference on Intelligent Virtual Agents
Situated incremental natural language understanding using Markov Logic Networks
Computer Speech and Language
| Hi-index | 0.00 |
Incremental natural language understanding is the task of assigning semantic representations to successively larger prefixes of utterances. We compare two types of statistical models for this task: a) local models, which predict a single class for an input; and b), sequential models, which align a sequence of classes to a sequence of input tokens. We show that, with some modifications, the first type of model can be improved and made to approximate the output of the second, even though the latter is more informative. We show on two different data sets that both types of model achieve comparable performance (significantly better than a baseline), with the first type requiring simpler training data. Results for the first type of model have been reported in the literature; we show that for our kind of data our more sophisticated variant of the model performs better.