Attention, intentions, and the structure of discourse
Computational Linguistics
Pitch accent in context: predicting intonational prominence from text
Artificial Intelligence - Special volume on natural language processing
Empirical methods for artificial intelligence
Empirical methods for artificial intelligence
Limited attention and discourse structure
Computational Linguistics
International Journal of Human-Computer Studies - Special issue on collaboration, cooperation and conflict in dialogue systems
Intentional influences on object redescriptions in dialogue: evidence from an empirical study
Intentional influences on object redescriptions in dialogue: evidence from an empirical study
An empirical study on the generation of anaphora in Chinese
Computational Linguistics
Do the right thing . . . but expect the unexpected
Computational Linguistics - Special issue on natural language generation
A probabilistic genre-independent model of pronominalization
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
An empirical investigation of proposals in collaborative dialogues
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Investigating cue selection and placement in tutorial discourse
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Integrating Gricean and attentional constraints
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Towards Visually-Grounded Spoken Language Acquisition
ICMI '02 Proceedings of the 4th IEEE International Conference on Multimodal Interfaces
Generation of repeated references to discourse entities
ENLG '07 Proceedings of the Eleventh European Workshop on Natural Language Generation
Learning content selection rules for generating object descriptions in dialogue
Journal of Artificial Intelligence Research
The clarity-brevity trade-off in generating referring expressions
INLG '06 Proceedings of the Fourth International Natural Language Generation Conference
Building a semantically transparent corpus for the generation of referring expressions
INLG '06 Proceedings of the Fourth International Natural Language Generation Conference
Introducing shared tasks to NLG: the TUNA shared task evaluation challenges
Empirical methods in natural language generation
Generating referring expressions in context: the GREC task evaluation challenges
Empirical methods in natural language generation
Generating subsequent reference in shared visual scenes: computation vs. re-use
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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A fundamental function of any task-oriented dialogue system is the ability to generate nominal expressions that describe objects in the task domain. In this paper, we report results from using machine learning to train and test a nominal-expression generator on a set of 393 nominal descriptions from the COCONUT corpus of task-oriented design dialogues. Results show that we can achieve a 50% match to human performance as opposed to a 16% baseline for just guessing the most frequent type of nominal expression in the COCONUT corpus. To our surprise our results indicate that many of the central features of previously proposed selection models did not improve the performance of the learned nominal-expression generator.