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
Novel reordering approaches in phrase-based statistical machine translation
ParaText '05 Proceedings of the ACL Workshop on Building and Using Parallel Texts
Referring expression generation through attribute-based heuristics
ENLG '09 Proceedings of the 12th European Workshop on Natural Language Generation
Introducing shared tasks to NLG: the TUNA shared task evaluation challenges
Empirical methods in natural language generation
Information Processing and Management: an International Journal
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In the first REG competition, researchers proposed several general-purpose algorithms for attribute selection for referring expression generation. 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.