Interactive Machine Translation Based on Partial Statistical Phrase-based Alignments

State-of-the-art Machine Translation (MT) systems are still far from being perfect. An alternative is the so-called Interactive Machine Translation (IMT) framework. In this framework, the knowledge of a human translator is combined with a MT system. We present a new technique for IMT which is based on the generation of partial alignments at phrase-level. The proposed technique partially aligns the source sentence with the user prefix and then translates the unaligned portion of the source sentence. The generation of such partial alignments is driven by statistical phrase-based models. Our technique relies on the application of smoothing techniques over the phrase models to appropriately assign probabilities to unseen events. We report experiments investigating the impact of the different smoothing techniques in the accuracy of our system. In addition, we compare the results obtained by our system with those obtained by other well-known IMT systems.