A view of the EM algorithm that justifies incremental, sparse, and other variants
Proceedings of the NATO Advanced Study Institute on Learning in graphical models
Bi-modal sentence structure for language modeling
Speech Communication
Automatic labeling of semantic roles
Computational Linguistics
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Automatic classification of dialog acts with semantic classification trees and polygrams
Connectionist, Statistical, and Symbolic Approaches to Learning for Natural Language Processing
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Restricted representation of phrase structure grammar for building a tree annotated corpus of Korean
Natural Language Engineering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Factored language models and generalized parallel backoff
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
Probability in the Engineering and Informational Sciences
Modeling socio-cultural phenomena in discourse
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Automatic extraction of cue phrases for cross-corpus dialogue act classification
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
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Dialog act (DA) tags are useful for many applications in natural language processing and automatic speech recognition. In this work, we introduce hidden backoff models (HBMs) where a large generalized backoff model is trained, using an embedded expectation-maximization (EM) procedure, on data that is partially observed. We use HBMs as word models conditioned on both DAs and (hidden) DA-segments. Experimental results on the ICSI meeting recorder dialog act corpus show that our procedure can strictly increase likelihood on training data and can effectively reduce errors on test data. In the best case, test error can be reduced by 6.1% relative to our baseline, an improvement on previously reported models that also use prosody. We also compare with our own prosody-based model, and show that our HBM is competitive even without the use of prosody. We have not yet succeeded, however, in combining the benefits of both prosody and the HBM.