Improving On-line Handwritten Recognition using Translation Models in Multimodal Interactive Machine Translation

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 ngrams and word-based IBM models. The proposed system achieves an important boost in performance with respect to previous work.