Technical Note: \cal Q-Learning
Machine Learning
The spatial semantic hierarchy
Artificial Intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Apprenticeship learning via inverse reinforcement learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
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
Learning context-dependent mappings from sentences to logical form
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 2 - Volume 2
Where to go: interpreting natural directions using global inference
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Interpretation of Spatial Language in a Map Navigation Task
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Learning to win by reading manuals in a Monte-Carlo framework
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Spatial role labeling: Towards extraction of spatial relations from natural language
ACM Transactions on Speech and Language Processing (TSLP)
Bootstrapping semantic parsers from conversations
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Lexical generalization in CCG grammar induction for semantic parsing
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Learning to win by reading manuals in a monte-carlo framework
Journal of Artificial Intelligence Research
Learning high-level planning from text
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Fast online lexicon learning for grounded language acquisition
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Spice it up?: mining refinements to online instructions from user generated content
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Corpus-based interpretation of instructions in virtual environments
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
Learning to interpret natural language instructions
SIAC '12 Proceedings of the Second Workshop on Semantic Interpretation in an Actionable Context
Toward learning perceptually grounded word meanings from unaligned parallel data
SIAC '12 Proceedings of the Second Workshop on Semantic Interpretation in an Actionable Context
Unsupervised PCFG induction for grounded language learning with highly ambiguous supervision
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Learning dependency-based compositional semantics
Computational Linguistics
Learning from natural instructions
Machine Learning
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We present a system that learns to follow navigational natural language directions. Where traditional models learn from linguistic annotation or word distributions, our approach is grounded in the world, learning by apprenticeship from routes through a map paired with English descriptions. Lacking an explicit alignment between the text and the reference path makes it difficult to determine what portions of the language describe which aspects of the route. We learn this correspondence with a reinforcement learning algorithm, using the deviation of the route we follow from the intended path as a reward signal. We demonstrate that our system successfully grounds the meaning of spatial terms like above and south into geometric properties of paths.