Artificial Intelligence - Special volume on natural language processing
A collaborative planning model of intentional structure
Computational Linguistics
Journal of Artificial Intelligence Research
Probabilistic head-driven parsing for discourse structure
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Relevance in Cooperation and Conflict*
Journal of Logic and Computation
Annotating preferences in negotiation dialogues
SemEval '12 Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation
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We propose a method for modelling how dialogue moves influence and are influenced by the agents' preferences. We extract constraints on preferences and dependencies among them, even when they are expressed indirectly, by exploiting discourse structure. Our method relies on a study of 20 dialogues chosen at random from the Verbmobil corpus. We then test the algorithms predictions against the judgements of naive annotators on 3 random unseen dialogues. The average annotator-algorithm agreement and the average inter-annotator agreement show that our method is reliable.