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
Machine Translation
Trans Type: Development-Evaluation Cycles to Boost Translator's Productivity
Machine Translation
Discriminative training and maximum entropy models for statistical machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
TransType: text prediction for translators
HLT '02 Proceedings of the second international conference on Human Language Technology Research
Statistical approaches to computer-assisted translation
Computational Linguistics
A web-based interactive computer aided translation tool
ACLDemos '09 Proceedings of the ACL-IJCNLP 2009 Software Demonstrations
Online learning for interactive statistical machine translation
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
TransAhead: a writing assistant for CAT and CALL
EACL '12 Proceedings of the Demonstrations at the 13th Conference of the European Chapter of the Association for Computational Linguistics
TransAhead: a computer-assisted translation and writing tool
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Error-proof, high-performance, and context-aware gestures for interactive text edition
CHI '13 Extended Abstracts on Human Factors in Computing Systems
A Computer-Assisted Translation and Writing System
ACM Transactions on Asian Language Information Processing (TALIP)
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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, where the knowledge of a human translator is combined with the MT system. We present a statistical IMT system able to learn from user feedback by means of the application of online learning techniques. These techniques allow the MT system to update the parameters of the underlying models in real time. According to empirical results, our system outperforms the results of conventional IMT systems. To the best of our knowledge, this online learning capability has never been provided by previous IMT systems. Our IMT system is implemented in C++, JavaScript, and ActionScript; and is publicly available on the Web.