A maximum entropy approach to natural language processing
Computational Linguistics
Target-Text Mediated Interactive Machine Translation
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
On-Line Handwriting Recognition System for Tamil Handwritten Characters
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
Statistical approaches to computer-assisted translation
Computational Linguistics
Multimodal interactive transcription of text images
Pattern Recognition
Multimodal interactive machine translation
International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction
Computer-assisted translation using speech recognition
IEEE Transactions on Audio, Speech, and Language Processing
On multimodal interactive machine translation using speech recognition
ICMI '11 Proceedings of the 13th international conference on multimodal interfaces
Improving on-line handwritten recognition in interactive machine translation
Pattern Recognition
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In interactive machine translation (IMT), a human expert is integrated into the core of a machine translation (MT) system. The human expert interacts with the IMT system by partially correcting the errors of the system's output. Then, the system proposes a new solution. This process is repeated until the output meets the desired quality. In this scenario, the interaction is typically performed using the keyboard and the mouse. In this work, we present an alternative modality to interact within IMT systems by writing on a tactile display or using an electronic pen. An on-line handwritten text recognition (HTR) system has been specifically designed to operate with IMT systems. Our HTR system improves previous approaches in two main aspects. First, HTR decoding is tightly coupled with the IMT system. Second, the language models proposed are context aware, in the sense that they take into account the partial corrections and the source sentence by using a combination of n-grams and word-based IBM models. The proposed system achieves an important boost in performance with respect to previous work.