Machine Learning
Automated Planning: Theory & Practice
Automated Planning: Theory & Practice
BLEU: a method for automatic evaluation of machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Linking Semantic and Knowledge Representations in a Multi-Domain Dialogue System
Journal of Logic and Computation
Automatic learning and generation of social behavior from collective human gameplay
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Walk the talk: connecting language, knowledge, and action in route instructions
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Sentence planning for realtime navigational instructions
NAACL-Short '06 Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers
The metric-FF planning system: translating "Ignoring delete lists" to numeric state variables
Journal of Artificial Intelligence Research
mGPT: a probabilistic planner based on heuristic search
Journal of Artificial Intelligence Research
Interpreting written how-to instructions
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Reinforcement learning for mapping instructions to actions
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
Following directions using statistical machine translation
Proceedings of the 5th ACM/IEEE international conference on Human-robot interaction
Learning human multimodal dialogue strategies
Natural Language Engineering
Learning to follow navigational directions
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Report on the second NLG challenge on generating instructions in virtual environments (GIVE-2)
INLG '10 Proceedings of the 6th International Natural Language Generation Conference
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
CL system: giving instructions by corpus based selection
ENLG '11 Proceedings of the 13th European Workshop on Natural Language Generation
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Previous approaches to instruction interpretation have required either extensive domain adaptation or manually annotated corpora. This paper presents a novel approach to instruction interpretation that leverages a large amount of unannotated, easy-to-collect data from humans interacting with a virtual world. We compare several algorithms for automatically segmenting and discretizing this data into (utterance, reaction) pairs and training a classifier to predict reactions given the next utterance. Our empirical analysis shows that the best algorithm achieves 70% accuracy on this task, with no manual annotation required.