Pragmatics and natural language generation
Artificial Intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Pipelines and size constraints
Computational Linguistics
Enabling technology for multilingual natural language generation: the KPML development environment
Natural Language Engineering
A foundation for general-purpose natural language generation: sentence realization using probabilistic models of language
Empirically-based control of natural language generation
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Using machine learning to explore human multimodal clarification strategies
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Enriching OWL Ontologies with Linguistic and User-Related Annotations: The ELEON System
ICTAI '07 Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence - Volume 02
Generating spatio-temporal descriptions in pollen forecasts
EACL '06 Proceedings of the Eleventh Conference of the European Chapter of the Association for Computational Linguistics: Posters & Demonstrations
SimpleNLG: a realisation engine for practical applications
ENLG '09 Proceedings of the 12th European Workshop on Natural Language Generation
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
The Meteor metric for automatic evaluation of machine translation
Machine Translation
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In this paper, we propose a general way of constructing an NLG system that permits the systematic exploration of the effects of particular system choices on output quality. We call a system developed according to this model a Programmable Instrumented Generator (PIG). Although a PIG could be designed and implemented from scratch, it is likely that researchers would also want to create PIGs based on existing systems. We therefore propose an approach to "instrumenting" an NLG system so as to make it PIG-like. To experiment with the idea, we have produced code to support the "instrumenting" of any NLG system written in Java. We report on initial experiments with "instrumenting" two existing systems and attempting to "tune" them to produce text satisfying complex stylistic constraints.