Target-Text Mediated Interactive Machine Translation
Machine Translation
The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
HMM-based word alignment in statistical translation
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 2
Efficient search for interactive statistical machine translation
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
Discriminative training and maximum entropy models for statistical machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Minimum error rate training in statistical machine translation
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
FSA: an efficient and flexible C++ toolkit for finite state automata using on-demand computation
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Reordering constraints for phrase-based statistical machine translation
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Novel reordering approaches in phrase-based statistical machine translation
ParaText '05 Proceedings of the ACL Workshop on Building and Using Parallel Texts
Statistical approaches to computer-assisted translation
Computational Linguistics
Improving translation via targeted paraphrasing
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Using targeted paraphrasing and monolingual crowdsourcing to improve translation
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Sections on Paraphrasing; Intelligent Systems for Socially Aware Computing; Social Computing, Behavioral-Cultural Modeling, and Prediction
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State-of-the-art computer-assisted translation engines are based on a statistical prediction engine, which interactively provides completions to what a human translator types. The integration of human speech into a computer-assisted system is also a challenging area and is the aim of this paper. So far, only a few methods for integrating statistical machine translation (MT) models with automatic speech recognition (ASR) models have been studied. They were mainly based on N-best rescoring approach. N-best rescoring is not an appropriate search method for building a real-time prediction engine. In this paper, we study the incorporation of MT models and ASR models using finite-state automata. We also propose some transducers based on MT models for rescoring the ASR word graphs.