Attention, intentions, and the structure of discourse
Computational Linguistics
Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems
Pitch accent in context: predicting intonational prominence from text
Artificial Intelligence - Special volume on natural language processing
Collaborating on referring expressions
Computational Linguistics
Limited attention and discourse structure
Computational Linguistics
The effect of resource limits and task complexity on collaborative planning in dialogue
Artificial Intelligence - Special volume on empirical methods
International Journal of Human-Computer Studies - Special issue on collaboration, cooperation and conflict in dialogue systems
Planning English Sentences
Generating referring expressions: boolean extensions of the incremental algorithm
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
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
Learning features that predict cue usage
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Generation that exploits corpus-based statistical knowledge
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Donnellan's distinction and a computational model of reference
ACL '86 Proceedings of the 24th annual meeting on Association for Computational Linguistics
Some pragmatic issues in the planning of definite and indefinite noun phrases
ACL '85 Proceedings of the 23rd annual meeting on Association for Computational Linguistics
Exploiting a probabilistic hierarchical model for generation
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Empirically estimating order constraints for content planning in generation
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Generating minimal definite descriptions
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Instance-based natural language generation
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
The order of prenominal adjectives in natural language generation
ACL '00 Proceedings of the 38th 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
The use of knowledge preconditions in language processing
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Integrating Gricean and attentional constraints
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Learning trees and rules with set-valued features
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Learning to interpret utterances using dialogue history
EACL '09 Proceedings of the 12th Conference of the European Chapter 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
A Japanese corpus of referring expressions used in a situated collaboration task
ENLG '09 Proceedings of the 12th European Workshop on Natural Language Generation
IVA '09 Proceedings of the 9th International Conference on Intelligent Virtual Agents
Individual and domain adaptation in sentence planning for dialogue
Journal of Artificial Intelligence Research
Gesture salience as a hidden variable for coreference resolution and keyframe extraction
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
Towards an extrinsic evaluation of referring expressions in situated dialogs
INLG '10 Proceedings of the 6th International Natural Language Generation Conference
Introducing shared tasks to NLG: the TUNA shared task evaluation challenges
Empirical methods in 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
A cross-linguistic study on the production of multimodal referring expressions in dialogue
ENLG '11 Proceedings of the 13th European Workshop on Natural Language Generation
Content selection from an ontology-based knowledge base for the generation of football summaries
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
GRE3D7: a corpus of distinguishing descriptions for objects in visual scenes
UCNLG+EVAL '11 Proceedings of the UCNLG+Eval: Language Generation and Evaluation Workshop
International Journal of Human-Computer Studies
Learning preferences for referring expression generation: effects of domain, language and algorithm
INLG '12 Proceedings of the Seventh International Natural Language Generation Conference
Content selection from semantic web data
INLG '12 Proceedings of the Seventh International Natural Language Generation Conference
REX-J: Japanese referring expression corpus of situated dialogs
Language Resources and Evaluation
A task-performance evaluation of referring expressions in situated collaborative task dialogues
Language Resources and Evaluation
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A fundamental requirement of any task-oriented dialogue system is the ability to generate object descriptions that refer to objects in the task domain. The subproblem of content selection for object descriptions in task-oriented dialogue has been the focus of much previous work and a large number of models have been proposed. In this paper, we use the annotated COCONUT corpus of task-oriented design dialogues to develop feature sets based on Dale and Reiter's (1995) incremental model, Brennan and Clark's (1996) conceptual pact model, and Jordan's (2000b) intentional influences model, and use these feature sets in a machine learning experiment to automatically learn a model of content selection for object descriptions. Since Dale and Reiter's model requires a representation of discourse structure, the corpus annotations are used to derive a representation based on Grosz and Sidner's (1986) theory of the intentional structure of discourse, as well as two very simple representations of discourse structure based purely on recency. We then apply the rule-induction program RIPPER to train and test the content selection component of an object description generator on a set of 393 object descriptions from the corpus. To our knowledge, this is the first reported experiment of a trainable content selection component for object description generation in dialogue. Three separate content selection models that are based on the three theoretical models, all independently achieve accuracies significantly above the MAJORITY CLASS baseline (17%) on unseen test data, with the intentional influences model (42.4%) performing significantly better than either the incremental model (30.4%) or the conceptual pact model (28.9%). But the best performing models combine all the feature sets, achieving accuracies near 60%. Surprisingly, a simple recency-based representation of discourse structure does as well as one based on intentional structure. To our knowledge, this is also the first empirical comparison of a representation of Grosz and Sidner's model of discourse structure with a simpler model for any generation task.