A view of the EM algorithm that justifies incremental, sparse, and other variants
Proceedings of the NATO Advanced Study Institute on Learning in graphical models
Target-Text Mediated Interactive Machine Translation
Machine Translation
A systematic comparison of various statistical alignment models
Computational Linguistics
Trans Type: Development-Evaluation Cycles to Boost Translator's Productivity
Machine Translation
The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
An empirical study of smoothing techniques for language modeling
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
HMM-based word alignment in statistical translation
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 2
Discriminative training and maximum entropy models for statistical machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Statistical phrase-based translation
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Extensions to HMM-based statistical word alignment models
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
An end-to-end discriminative approach to machine translation
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
The scaling problem in the pattern recognition approach to machine translation
Pattern Recognition Letters
Statistical approaches to computer-assisted translation
Computational Linguistics
Online large-margin training of syntactic and structural translation features
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Interactive pattern recognition
MLMI'07 Proceedings of the 4th international conference on Machine learning for multimodal interaction
Online learning via dynamic reranking for computer assisted translation
CICLing'11 Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part II
An interactive machine translation system with online learning
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: Systems Demonstrations
Passive-aggressive for on-line learning in statistical machine translation
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
An active learning scenario for interactive machine translation
ICMI '11 Proceedings of the 13th international conference on multimodal interfaces
Active learning for interactive machine translation
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
A Computer-Assisted Translation and Writing System
ACM Transactions on Asian Language Information Processing (TALIP)
Improving on-line handwritten recognition in interactive machine translation
Pattern Recognition
Cost-sensitive active learning for computer-assisted translation
Pattern Recognition Letters
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State-of-the-art Machine Translation (MT) systems are still far from being perfect. An alternative is the so-called Interactive Machine Translation (IMT) framework. In this framework, the knowledge of a human translator is combined with a MT system. The vast majority of the existing work on IMT makes use of the well-known batch learning paradigm. In the batch learning paradigm, the training of the IMT system and the interactive translation process are carried out in separate stages. This paradigm is not able to take advantage of the new knowledge produced by the user of the IMT system. In this paper, we present an application of the online learning paradigm to the IMT framework. In the online learning paradigm, the training and prediction stages are no longer separated. This feature is particularly useful in IMT since it allows the user feedback to be taken into account. The online learning techniques proposed here incrementally update the statistical models involved in the translation process. Empirical results show the great potential of online learning in the IMT framework.