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Miguel Domingo, Álvaro Peris, Francisco Casacuberta. Segment-based interactive-predictive machine translation. Machine Translation Journal, 2017. Vol. 31 pp. 163-185.

Machine translation systems require human revision to obtain high-quality translations. Interactive methods provide an efficient human–computer collaboration, notably increasing productivity. Recently, new interactive protocols have been proposed, seeking for a more effective user interaction with the system. In this work, we present one of these new protocols, which allows the user to validate all correct word sequences in a translation hypothesis. Thus, the left-to-right barrier from most of the existing protocols is broken. We compare this protocol against the classical prefix-based approach, obtaining a significant reduction of the user effort in a simulated environment. Additionally, we experiment with the use of confidence measures to select the word the user should correct at each iteration, reaching the conclusion that the order in which words are corrected does not affect the overall effort.