Building natural language generation systems
Building natural language generation systems
Graph-based generation of referring expressions
Computational Linguistics
Forest-based statistical sentence generation
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Exploiting a probabilistic hierarchical model for generation
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Generating referring expressions in open domains
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
The Pyramid Method: Incorporating human content selection variation in summarization evaluation
ACM Transactions on Speech and Language Processing (TSLP)
Automatic evaluation of machine translation quality using n-gram co-occurrence statistics
HLT '02 Proceedings of the second international conference on Human Language Technology Research
Novel reordering approaches in phrase-based statistical machine translation
ParaText '05 Proceedings of the ACL Workshop on Building and Using Parallel Texts
Introducing shared tasks to NLG: the TUNA shared task evaluation challenges
Empirical methods in natural language generation
Does size matter: how much data is required to train a REG algorithm?
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Learning preferences for referring expression generation: effects of domain, language and algorithm
INLG '12 Proceedings of the Seventh International Natural Language Generation Conference
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Previous work in referring expression generation has explored general purpose techniques for attribute selection and surface realization. However, most of this work did not take into account: a) stylistic differences between speakers; or b) trainable surface realization approaches that combine semantic and word order information. In this paper we describe and evaluate several end-to-end referring expression generation algorithms that take into consideration speaker style and use data-driven surface realization techniques.