Plan Recognition in Natural Language Dialogue
Plan Recognition in Natural Language Dialogue
Automatic labeling of semantic roles
Computational Linguistics
Negotiation over tasks in hybrid human-agent teams for simulation-based training
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Probabilistic grammars for plan recognition
Probabilistic grammars for plan recognition
The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
Hidden understanding models of natural language
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Probabilistic grounding of situated speech using plan recognition and reference resolution
ICMI '05 Proceedings of the 7th international conference on Multimodal interfaces
Connecting language to the world
Artificial Intelligence - Special volume on connecting language to the world
Maximum entropy models for FrameNet classification
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
A statistical semantic parser that integrates syntax and semantics
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Intentional context in situated natural language learning
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
The lumière project: Bayesian user modeling for inferring the goals and needs of software users
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Tracking dragon-hunters with language models
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Semi-automatic task recognition for interactive narratives with EAT & RUN
Proceedings of the Intelligent Narrative Technologies III Workshop
Semi-automated dialogue act classification for situated social agents in games
Agents for games and simulations II
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We introduce a framework for learning situated Natural Language Interfaces (NLIs) to interactive virtual environments. The framework exploits the non-linguistic context, or situation, explicitly modeled in such interactive applications. This situation model is integrated with a model of word meaning in a principled manner using a noisy channel approach to language understanding. Preliminary experimentation in an independently designed interactive application, i.e. the Mission Rehearsal Exercise (MRE), shows that this situated NLI outperforms a state of the art NLI on both whole frame accuracy and F-Score metrics. Further, use of the situation model in the situated NLI is shown to increase robustness to the noise introduced by the use of automatic speech recognition.