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Beyond Prefix-Based Interactive Translation Prediction. Proceedings of the SIGNLL Conference on Computational Natural Language Learning (CoNLL), 2016. pp. 198-207. ACL.Current automatic machine translation systems require heavy human proof- reading to produce high-quality transla- tions. We present a new interactive ma- chine translation approach aimed at pro- viding a natural collaboration between hu- mans and translation systems. As such, we grant the user complete freedom to vali- date and correct any part of the translations suggested by the system. Our approach is then designed according to the require- ments placed by this unrestricted proof- reading protocol. In particular, the ability of the system to suggest new translations coherent with the set of potentially disjoint translation segments validated by the user. We evaluate our approach in a user- simulated setting where reference transla- tions are considered the output desired by a human expert. Results show important reductions in the number of edits in com- parison to decoupled post-editing and con- ventional prefix-based interactive transla- tion prediction. Additionally, we provide evidence that it can also reduce the cogni- tive overload reported for interactive trans- lation systems in previous user studies.