Assessing agreement on classification tasks: the kappa statistic
Computational Linguistics
Making large-scale support vector machine learning practical
Advances in kernel methods
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Evaluating a trainable sentence planner for a spoken dialogue system
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
SPoT: a trainable sentence planner
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
A classification of dialogue actions in tutorial dialogue
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
ITSPOKE: an intelligent tutoring spoken dialogue system
HLT-NAACL--Demonstrations '04 Demonstration Papers at HLT-NAACL 2004
Discovering Tutorial Dialogue Strategies with Hidden Markov Models
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
To Elicit Or To Tell: Does It Matter?
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
Good question! Statistical ranking for question generation
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
My science tutor: A conversational multimedia virtual tutor for elementary school science
ACM Transactions on Speech and Language Processing (TSLP)
DISCUSS: a dialogue move taxonomy layered over semantic representations
IWCS '11 Proceedings of the Ninth International Conference on Computational Semantics
Automatic gap-fill question generation from text books
IUNLPBEA '11 Proceedings of the 6th Workshop on Innovative Use of NLP for Building Educational Applications
Predicting change in student motivation by measuring cohesion between tutor and student
IUNLPBEA '11 Proceedings of the 6th Workshop on Innovative Use of NLP for Building Educational Applications
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part I
AutoTutor: an intelligent tutoring system with mixed-initiative dialogue
IEEE Transactions on Education
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A key challenge for dialogue-based intelligent tutoring systems lies in selecting follow-up questions that are not only context relevant but also encourage self-expression and stimulate learning. This paper presents an approach to ranking candidate questions for a given dialogue context and introduces an evaluation framework for this task. We learn to rank using judgments collected from expert human tutors, and we show that adding features derived from a rich, multi-layer dialogue act representation improves system performance over baseline lexical and syntactic features to a level in agreement with the judges. The experimental results highlight the important factors in modeling the questioning process. This work provides a framework for future work in automatic question generation and it represents a step toward the larger goal of directly learning tutorial dialogue policies directly from human examples.