Graph-based generation of referring expressions
Computational Linguistics
Cooking up referring expressions
ACL '89 Proceedings of the 27th annual meeting on Association for Computational Linguistics
A best-first search algorithm for generating referring expressions
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 2
Generating minimal definite descriptions
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
INLG '00 Proceedings of the first international conference on Natural language generation - Volume 14
Generating Referring Expressions that Involve Gradable Properties
Computational Linguistics
Incremental generation of spatial referring expressions in situated dialog
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Evaluating algorithms for the generation of referring expressions using a balanced corpus
ENLG '07 Proceedings of the Eleventh European Workshop on Natural Language Generation
The TUNA-REG Challenge 2009: overview and evaluation results
ENLG '09 Proceedings of the 12th European Workshop on Natural Language Generation
Learning content selection rules for generating object descriptions in dialogue
Journal of Artificial Intelligence Research
The use of spatial relations in referring expression generation
INLG '08 Proceedings of the Fifth International Natural Language Generation Conference
The TUNA challenge 2008: overview and evaluation results
INLG '08 Proceedings of the Fifth International Natural Language Generation Conference
Identifying objects on the basis of spatial contrast: an empirical study
SC'04 Proceedings of the 4th international conference on Spatial Cognition: reasoning, Action, Interaction
The impact of visual context on the content of referring expressions
ENLG '11 Proceedings of the 13th European Workshop on Natural Language Generation
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Recent years have seen a trend towards empirically motivated and more data-driven approaches in the field of referring expression generation (REG). Much of this work has focussed on initial reference to objects in visual scenes. While this scenario of use is one of the strongest contenders for real-world applications of referring expression generation, existing data sets still only embody very simple stimulus scenes. To move this research forward, we require data sets built around increasingly complex scenes, and we need much larger data sets to accommodate their higher dimensionality. To control the complexity, we also need to adopt a hypothesis-driven approach to scene design. In this paper, we describe GRE3D7, the largest corpus of human-produced distinguishing descriptions available to date, discuss the hypotheses that underlie its design, and offer a number of analyses of the 4480 descriptions it contains.