Attention, intentions, and the structure of discourse
Computational Linguistics
Graph-based generation of referring expressions
Computational Linguistics
Intentional influences on object redescriptions in dialogue: evidence from an empirical study
Intentional influences on object redescriptions in dialogue: evidence from an empirical study
Cooking up referring expressions
ACL '89 Proceedings of the 27th annual meeting on Association for Computational Linguistics
Learning attribute selections for non-pronominal expressions
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
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
Learning content selection rules for generating object descriptions in dialogue
Journal of Artificial Intelligence Research
Noun phrase generation for situated dialogs
INLG '06 Proceedings of the Fourth International Natural Language Generation Conference
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
Preferences versus adaptation during referring expression generation
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
Computational generation of referring expressions: A survey
Computational Linguistics
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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Traditional computational approaches to referring expression generation operate in a deliberate manner, choosing the attributes to be included on the basis of their ability to distinguish the intended referent from its distractors. However, work in psycholinguistics suggests that speakers align their referring expressions with those used previously in the discourse, implying less deliberate choice and more subconscious reuse. This raises the question as to which is a more accurate characterisation of what people do. Using a corpus of dialogues containing 16,358 referring expressions, we explore this question via the generation of subsequent references in shared visual scenes. We use a machine learning approach to referring expression generation and demonstrate that incorporating features that correspond to the computational tradition does not match human referring behaviour as well as using features corresponding to the process of alignment. The results support the view that the traditional model of referring expression generation that is widely assumed in work on natural language generation may not in fact be correct; our analysis may also help explain the oft-observed redundancy found in human-produced referring expressions.