Statistical approaches to computer-assisted translation
Computational Linguistics
Human interaction for high-quality machine translation
Communications of the ACM - A View of Parallel Computing
Interactive predictive parsing
IWPT '09 Proceedings of the 11th International Conference on Parsing Technologies
Interactive pattern recognition
MLMI'07 Proceedings of the 4th international conference on Machine learning for multimodal interaction
Interactive predictive parsing using a web-based architecture
HLT-DEMO '10 Proceedings of the NAACL HLT 2010 Demonstration Session
Multi-modal computer assisted speech transcription
International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction
IEEE Transactions on Audio, Speech, and Language Processing
Confidence measures for error discrimination in an interactive predictive parsing framework
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
On multimodal interactive machine translation using speech recognition
ICMI '11 Proceedings of the 13th international conference on multimodal interfaces
Sketch-editing games: human-machine communication, game theory and applications
Proceedings of the 25th annual ACM symposium on User interface software and technology
Improving on-line handwritten recognition in interactive machine translation
Pattern Recognition
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Current machine translation systems are far from being perfect. However, such systems can be used in computer-assisted translation to increase the productivity of the (human) translation process. The idea is to use a text-to-text translation system to produce portions of target language text that can be accepted or amended by a human translator using text or speech. These user-validated portions are then used by the text-to-text translation system to produce further, hopefully improved suggestions. There are different alternatives of using speech in a computer-assisted translation system: From pure dictated translation to simple determination of acceptable partial translations by reading parts of the suggestions made by the system. In all the cases, information from the text to be translated can be used to constrain the speech decoding search space. While pure dictation seems to be among the most attractive settings, unfortunately perfect speech decoding does not seem possible with the current speech processing technology and human error-correcting would still be required. Therefore, approaches that allow for higher speech recognition accuracy by using increasingly constrained models in the speech recognition process are explored here. All these approaches are presented under the statistical framework. Empirical results support the potential usefulness of using speech within the computer-assisted translation paradigm.