A Bayesian/Information Theoretic Model of Learning to Learn viaMultiple Task Sampling
Machine Learning - Special issue on inductive transfer
Machine Learning - Special issue on inductive transfer
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Three new probabilistic models for dependency parsing: an exploration
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Learning Multiple Tasks with Kernel Methods
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Learning Gaussian processes from multiple tasks
ICML '05 Proceedings of the 22nd international conference on Machine learning
Head-Driven Statistical Models for Natural Language Parsing
Computational Linguistics
Online large-margin training of dependency parsers
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Multi-Task Learning for Classification with Dirichlet Process Priors
The Journal of Machine Learning Research
Learning a meta-level prior for feature relevance from multiple related tasks
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NAACL-Short '06 Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers
Domain adaptation for statistical classifiers
Journal of Artificial Intelligence Research
Domain adaptive bootstrapping for named entity recognition
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
Automatic domain adaptation for parsing
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Cross-language text classification using structural correspondence learning
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Phylogenetic grammar induction
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Domain adaptation of maximum entropy language models
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
Domain adaptation meets active learning
ALNLP '10 Proceedings of the NAACL HLT 2010 Workshop on Active Learning for Natural Language Processing
Discriminative instance weighting for domain adaptation in statistical machine translation
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
The necessity of combining adaptation methods
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Domain adaptation for text categorization by feature labeling
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Is machine translation ripe for cross-lingual sentiment classification?
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Language models as representations for weakly-supervised NLP tasks
CoNLL '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning
Adapting text instead of the model: an open domain approach
CoNLL '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning
Cross-Lingual Adaptation Using Structural Correspondence Learning
ACM Transactions on Intelligent Systems and Technology (TIST)
Cross-Domain Effects on Parse Selection for Precision Grammars
Research on Language and Computation
Modeling of stylistic variation in social media with stretchy patterns
DIALECTS '11 Proceedings of the First Workshop on Algorithms and Resources for Modelling of Dialects and Language Varieties
Domain adaptation with ensemble of feature groups
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Structural and topical dimensions in multi-task patent translation
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
Multi-domain learning: when do domains matter?
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
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Multi-task learning is the problem of maximizing the performance of a system across a number of related tasks. When applied to multiple domains for the same task, it is similar to domain adaptation, but symmetric, rather than limited to improving performance on a target domain. We present a more principled, better performing model for this problem, based on the use of a hierarchical Bayesian prior. Each domain has its own domain-specific parameter for each feature but, rather than a constant prior over these parameters, the model instead links them via a hierarchical Bayesian global prior. This prior encourages the features to have similar weights across domains, unless there is good evidence to the contrary. We show that the method of (Daumé III, 2007), which was presented as a simple "preprocessing step," is actually equivalent, except our representation explicitly separates hyperparameters which were tied in his work. We demonstrate that allowing different values for these hyperparameters significantly improves performance over both a strong baseline and (Daumé III, 2007) within both a conditional random field sequence model for named entity recognition and a discriminatively trained dependency parser.