Getting the message across in RST-based text generation
Current research in natural language generation
An evaluation of explanations of probabilistic inference
Computers and Biomedical Research - Papers presented at the 16th symposium on computer applications in medical care (SCAMC)
Decision-analytic intelligent systems: automated explanation and knowledge acquisition
Decision-analytic intelligent systems: automated explanation and knowledge acquisition
Specifying intonation from context for speech synthesis
Speech Communication
A plan-based model for response generation in collaborative task-oriented dialogues
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Expressing rhetorical relations in instructional text: a case study of the purpose relation
Computational Linguistics
Centering: a framework for modeling the local coherence of discourse
Computational Linguistics
Empirically designing and evaluating a new revision-based model for summary generation
Artificial Intelligence - Special volume on empirical methods
Decision quality using ranked attribute weights
Management Science
Bayesian reasoning in an abductive mechanism for argument generation and analysis
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Using Grice's maxim of quantity to select the content of plan descriptions
Artificial Intelligence
Building natural language generation systems
Building natural language generation systems
Artificial Intelligence
Learning an Agent's Utility Function by Observing Behavior
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Lessons from a failure: generating tailored smoking cessation letters
Artificial Intelligence
Visual exploration and incremental utility elicitation
Eighteenth national conference on Artificial intelligence
Similarity of personal preferences: theoretical foundations and empirical analysis
Artificial Intelligence
Developing and empirically evaluating robust explanation generators: the KNIGHT experiments
Computational Linguistics
Floating constraints in lexical choice
Computational Linguistics
Collaborative response generation in planning dialogues
Computational Linguistics - Special issue on natural language generation
Automatic language and information processing: rethinking evaluation
Natural Language Engineering
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
International Journal of Human-Computer Studies
Towards automatic generation of natural language generation systems
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Learning extraction patterns for subjective expressions
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Toward an information visualization workspace: combining multiple means of expression
Human-Computer Interaction
An empirical study of the influence of user tailoring on evaluative argument effectiveness
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Adaptive provision of evaluation-oriented information: tasks and techniques
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Group decision making through mediated discussions
UM'03 Proceedings of the 9th international conference on User modeling
Argumentation in artificial intelligence
Artificial Intelligence
Using arguments for making and explaining decisions
Artificial Intelligence
Being Old Doesn’t Mean Acting Old: How Older Users Interact with Spoken Dialog Systems
ACM Transactions on Accessible Computing (TACCESS)
Reducing working memory load in spoken dialogue systems
Interacting with Computers
Individual and domain adaptation in sentence planning for dialogue
Journal of Artificial Intelligence Research
Generating tailored, comparative descriptions with contextually appropriate intonation
Computational Linguistics
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Evaluative arguments are pervasive in natural human communication. In countless situations people attempt to advise or persuade their interlocutors that something is desirable (vs. undesirable) or fight (vs. wrong). With the proliferation of on-line systems serving as personal advisors and assistants, there is a pressing need to develop general and testable computational models for generating and presenting evaluative arguments. Previous research on generating evaluative arguments has been characterized by two major limitations. First, researchers have tended to focus only on specific aspects of the generation process. Second, the proposed approaches were not empirically tested. The research presented in this paper addresses both limitations. We have designed and implemented a complete computational model for generating evaluative arguments. For content selection and organization, we devised an argumentation strategy based on guidelines from argumentation theory. For expressing the content in natural language, we extended and integrated previous work in computational linguistics on generating evaluative arguments. The key knowledge source for both tasks is a quantitative model of user preferences. To empirically test critical aspects of our generation model, we have devised and implemented an evaluation framework in which the effectiveness of evaluative arguments can be measured with real users. Within the framework, we have performed an experiment to test two basic hypotheses on which the design of the computational model is based; namely, that our proposal for tailoring an evaluative argument to the addressee's preferences increases its effectiveness, and that differences in conciseness significantly influence argument effectiveness. The second hypothesis was confirmed in the experiment. In contrast, the first hypothesis was only marginally confirmed. However, independent testing by other researchers has recently provided further support for this hypothesis.