Building natural language generation systems
Building natural language generation systems
Graph-based generation of referring expressions
Computational Linguistics
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
The order of prenominal adjectives in natural language generation
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
A Reference Architecture for Natural Language Generation Systems
Natural Language Engineering
Evaluating algorithms for the generation of referring expressions using a balanced corpus
ENLG '07 Proceedings of the Eleventh European Workshop on Natural Language Generation
Class-based ordering of prenominal modifiers
ENLG '09 Proceedings of the 12th European Workshop on Natural Language Generation
Learning lexical alignment policies for generating referring expressions in spoken dialogue systems
ENLG '09 Proceedings of the 12th European Workshop on Natural Language Generation
An alignment-capable microplanner for natural language generation
ENLG '09 Proceedings of the 12th European Workshop on Natural Language Generation
Computational generation of referring expressions: A survey
Computational Linguistics
Generating subsequent reference in shared visual scenes: computation vs. re-use
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
The impact of visual context on the content of referring expressions
ENLG '11 Proceedings of the 13th European Workshop on Natural Language Generation
Direction giving: an attempt to increase user engagement
ENLG '11 Proceedings of the 13th European Workshop on Natural Language Generation
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Current Referring Expression Generation algorithms rely on domain dependent preferences for both content selection and linguistic realization. We present two experiments showing that human speakers may opt for dispreferred properties and dispreferred modifier orderings when these were salient in a preceding interaction (without speakers being consciously aware of this). We discuss the impact of these findings for current generation algorithms.