Incremental and Adaptive Learning for Interactive Machine Translation

Abstract

High-quality translation between any pair of languages can be achieved by human post-editing of the outputs of a MT system or, as mentioned in Chap. 6, by following the Interactive Machine Translation (IMT) approach. In the interactive pattern recognition framework, IMT can predict the translation of the next words in the output, and can suggest them to the human translator who, iteratively, can accept or correct the suggested translations. The consolidated translations obtained through the successive steps of the interaction process can be considered as “perfect translations” due to the fact that they have been validated by a human expert. Therefore, this consolidated translations can easily be converted into new, fresh, training data, useful for dynamically adapting the system to the changing environment. Taking that into account, on the one hand, the IMT paradigm offers an appropriate framework for incremental and adaptive learning in SMT. On the other hand, incremental and adaptive learning offers the possibility to substantially save human effort by simply avoiding the user to perform the same corrections again and again.