Evaluation of spoken language systems: the ATIS domain
HLT '90 Proceedings of the workshop on Speech and Natural Language
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Efficient noise-tolerant learning from statistical queries
STOC '93 Proceedings of the twenty-fifth annual ACM symposium on Theory of computing
Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
Selective Sampling Using the Query by Committee Algorithm
Machine Learning
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Statistical methods for speech recognition
Statistical methods for speech recognition
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Analyzing the effectiveness and applicability of co-training
Proceedings of the ninth international conference on Information and knowledge management
Spoken Language Processing: A Guide to Theory, Algorithm, and System Development
Spoken Language Processing: A Guide to Theory, Algorithm, and System Development
Automated Natural Spoken Dialog
Computer
Machine Learning
Machine Learning
Active + Semi-supervised Learning = Robust Multi-View Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Combining Labeled and Unlabeled Data for MultiClass Text Categorization
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Active Learning for Natural Language Parsing and Information Extraction
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Less is More: Active Learning with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Employing EM and Pool-Based Active Learning for Text Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Detecting errors within a corpus using anomaly detection
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Active learning for statistical natural language parsing
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
An empirical study of active learning with support vector machines for Japanese word segmentation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Active learning for HPSG parse selection
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Active learning with committees for text categorization
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Towards Scalable Dataset Construction: An Active Learning Approach
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
A novel traffic analysis for identifying search fields in the long tail of web sites
Proceedings of the 19th international conference on World wide web
Pattern classification and clustering: A review of partially supervised learning approaches
Pattern Recognition Letters
Effective balancing error and user effort in interactive handwriting recognition
Pattern Recognition Letters
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State of the art data-driven speech and language processing systems require a large amount of human intervention ranging from data annotation to system prototyping. In the traditional supervised passive approach, the system is trained on a given number of annotated data samples and evaluated using a separate test set. Then more data is collected arbitrarily, annotated, and the whole cycle is repeated. In this article, we propose the active approach where the system itself selects its own training data, evaluates itself and re-trains when necessary. We first employ active learning which aims to automatically select the examples that are likely to be the most informative for a given task. We use active learning for both selecting the examples to label and the examples to re-label in order to correct labeling errors. Furthermore, the system automatically evaluates itself using active evaluation to keep track of the unexpected events and decides on-demand to label more examples. The active approach enables dynamic adaptation of spoken language processing systems to unseen or unexpected events for nonstationary input while reducing the manual annotation effort significantly. We have evaluated the active approach with the AT&T spoken dialog system used for customer care applications. In this article, we present our results for both automatic speech recognition and spoken language understanding.