Foundations of statistical natural language processing
Foundations of statistical natural language processing
Large Margin Classification Using the Perceptron Algorithm
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Neuro-Dynamic Programming
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Algorithms for Inverse Reinforcement Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Discriminative Reranking for Natural Language Parsing
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
Apprenticeship learning via inverse reinforcement learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
A parsing: fast exact Viterbi parse selection
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Learning structured prediction models: a large margin approach
ICML '05 Proceedings of the 22nd international conference on Machine learning
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Prediction, Learning, and Games
Prediction, Learning, and Games
ICML '06 Proceedings of the 23rd international conference on Machine learning
Single-pass online learning: performance, voting schemes and online feature selection
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Incremental parsing with the perceptron algorithm
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Coarse-to-fine n-best parsing and MaxEnt discriminative reranking
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Advances in discriminative parsing
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Exponentiated gradient algorithms for log-linear structured prediction
Proceedings of the 24th international conference on Machine learning
Pegasos: Primal Estimated sub-GrAdient SOlver for SVM
Proceedings of the 24th international conference on Machine learning
Predicting Structured Data (Neural Information Processing)
Predicting Structured Data (Neural Information Processing)
Practical structured learning techniques for natural language processing
Practical structured learning techniques for natural language processing
Learning and inference for hierarchically split PCFGs
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Maximum entropy inverse reinforcement learning
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Guest editorial: special issue on structured prediction
Machine Learning
Learning from demonstration using MDP induced metrics
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Inverse Reinforcement Learning in Partially Observable Environments
The Journal of Machine Learning Research
Forest reranking through subtree ranking
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Inverse reinforcement learning for interactive systems
Proceedings of the 2nd Workshop on Machine Learning for Interactive Systems: Bridging the Gap Between Perception, Action and Communication
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One major idea in structured prediction is to assume that the predictor computes its output by finding the maximum of a score function. The training of such a predictor can then be cast as the problem of finding weights of the score function so that the output of the predictor on the inputs matches the corresponding structured labels on the training set. A similar problem is studied in inverse reinforcement learning (IRL) where one is given an environment and a set of trajectories and the problem is to find a reward function such that an agent acting optimally with respect to the reward function would follow trajectories that match those in the training set. In this paper we show how IRL algorithms can be applied to structured prediction, in particular to parser training. We present a number of recent incremental IRL algorithms in a unified framework and map them to parser training algorithms. This allows us to recover some existing parser training algorithms, as well as to obtain a new one. The resulting algorithms are compared in terms of their sensitivity to the choice of various parameters and generalization ability on the Penn Treebank WSJ corpus.