Abstract
Achieving high-quality translation between any pair of languages is not possible with the current Machine-Translation (MT) technology a human post-editing of the outputs of the MT system being necessary. Therefore, MT is a suitable area to apply the Interactive Pattern Recognition (IPR) framework and this application has led to what nowadays is known as Interactive Machine Translation (IMT). IMT can predict the translation of a given source sentence, and the human translator can accept or correct some of the errors. The text amended by the human translator can be used by the system to suggest new improved translations with the same translation models in an iterative process until the whole output is accepted by the human.
As in other areas where IPR is being applied, IMT offers a nice framework for adaptive learning. The consolidated translations obtained through the successive steps of the interaction process can easily be converted into new, fresh, training data, useful for dynamically adapting the system to the changing environment. On the other hand, IMT also allows one to take advantage of some available multi-modal interfaces to increase of productivity. Multi-modal interfaces and adaptive learning in IMT will be covered in Chaps. 7 and 8, respectively.
With Contribution Of: Jorge Civera, Jesús González-Rubio and Daniel Ortiz-Martínez.
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Notes
- 1.
Following the tradition in the error-correcting literature, it is assumed that the input data are a “distorted” version of the “correct” data represented by the model.
- 2.
Although multiple reference translations would be desirable; because of the high cost of obtaining alternative reference translations only one reference translation is usually at our disposal.
- 3.
The interested reader is referred to [7] for a detailed comparative of SMT evaluation measures.
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Toselli, A.H., Vidal, E., Casacuberta, F. (2011). Interactive Machine Translation. In: Multimodal Interactive Pattern Recognition and Applications. Springer, London. https://doi.org/10.1007/978-0-85729-479-1_6
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