ACL RD-TEC 1.0 Summarization of N06-1035
Paper Title:
COMPARING THE UTILITY OF STATE FEATURES IN SPOKEN DIALOGUE USING REINFORCEMENT LEARNING
COMPARING THE UTILITY OF STATE FEATURES IN SPOKEN DIALOGUE USING REINFORCEMENT LEARNING
Authors: Joel Tetreault and Diane Litman
Primarily assigned technology terms:
- algorithm
- approximation
- computational linguistics
- detection algorithm
- dialogue management
- dialogue manager
- dialogue system
- dialogue systems
- dialogue tutoring
- dynamic programming
- feature comparison
- final state
- frustration detection
- human language
- human language technology
- human-computer dialogue
- intelligent tutoring
- intelligent tutoring systems
- itspoke
- language technology
- learner
- learning
- learning techniques
- linear function approximation
- markov decision process
- nlp
- ranking
- reasoning
- recognition
- reinforcement learning
- speech recognition
- splitting
- spoken dialogue
- spoken dialogue systems
- spoken dialogue tutoring
- supervised learning
- tutoring system
- weighting
- why2-atlas
Other assigned terms:
- annotated corpus
- annotation
- annotator
- association for computational linguistics
- case
- clarification dialogue
- community
- concept
- concepts
- convergence
- data set
- dialog
- dialogue act
- dialogue acts
- dialogue context
- dialogue length
- dialogue state
- dialogues
- discourse
- emotion
- empirical results
- entropy
- essay
- evaluation metric
- fact
- feature
- inter-annotator agreement
- kappa
- linguistics
- manual annotation
- method
- methodology
- process
- question-answer format
- recognition errors
- signal
- speech recognition errors
- state feature
- technology
- term
- test corpus
- toolkit
- tutoring
- user
