Discriminative Reranking for Natural Language Parsing
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
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IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
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NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
ANLC '92 Proceedings of the third conference on Applied natural language processing
Sentence planning as description using tree adjoining grammar
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COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
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ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
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ANLP/NAACL-ConvSyst '00 Proceedings of the 2000 ANLP/NAACL Workshop on Conversational systems - Volume 3
An introduction to boosting and leveraging
Advanced lectures on machine learning
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
Natural language generation in dialog systems
HLT '01 Proceedings of the first international conference on Human language technology research
Towards automatic generation of natural language generation systems
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Evaluating a trainable sentence planner for a spoken dialogue system
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Ranking algorithms for named-entity extraction: boosting and the voted perceptron
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
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
Discriminative Reranking for Natural Language Parsing
Computational Linguistics
New developments in parsing technology
Hidden-variable models for discriminative reranking
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Aggregation via set partitioning for natural language generation
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Modeling local coherence: An entity-based approach
Computational Linguistics
Performance confidence estimation for automatic summarization
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Optimizing dialogue management with reinforcement learning: experiments with the NJFun system
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Practical grammar-based NLG from examples
INLG '08 Proceedings of the Fifth International Natural Language Generation Conference
Good question! Statistical ranking for question generation
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Rating computer-generated questions with Mechanical Turk
CSLDAMT '10 Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon's Mechanical Turk
Introduction to special issue on machine learning for adaptivity in spoken dialogue systems
ACM Transactions on Speech and Language Processing (TSLP)
Assessing user simulation for dialog systems using human judges and automatic evaluation measures
Natural Language Engineering
Instance-based generation for interactive restricted domain question answering systems
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
Question ranking and selection in tutorial dialogues
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
Generating natural language descriptions from OWL ontologies: the natural OWL system
Journal of Artificial Intelligence Research
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Sentence planning is a set of inter-related but distinct tasks, one of which is sentence scoping, i.e. the choice of syntactic structure for elementary speech acts and the decision of how to combine them into one or more sentences. In this paper, we present SPoT, a sentence planner, and a new methodology for automatically training SPoT on the basis of feedback provided by human judges. We reconceptualize the task into two distinct phases. First, a very simple, randomized sentence-plan-generator (SPG) generates a potentially large list of possible sentence plans for a given text-plan input. Second, the sentence-plan-ranker (SPR) ranks the list of output sentence plans, and then selects the top-ranked plan. The SPR uses ranking rules automatically learned from training data. We show that the trained SPR learns to select a sentence plan whose rating on average is only 5% worse than the top human-ranked sentence plan.