Segment-based interactive-predictive machine translation

[EN] 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.The research leading to these results has received funding from the Ministerio de Economia y Competitividad (MINECO) under Project CoMUN-HaT (Grant Agreement TIN2015-70924-C2-1-R), and Generalitat Valenciana under Project ALMAMATER (Ggrant Agreement PROMETEOII/2014/030).Domingo-Ballester, M.; Peris-Abril, Á.; Casacuberta Nolla, F. (2017). Segment-based interactive-predictive machine translation. Machine Translation. 31(4):163-185. https://doi.org/10.1007/s10590-017-9213-3S163185314Alabau V, Bonk R, Buck C, Carl M, Casacuberta F, García-Martínez M, González-Rubio J, Koehn P, Leiva LA, Mesa-Lao B, Ortiz-Martínez D, Saint-Amand H, Sanchis-Trilles G, Tsoukala C (2013) CASMACAT: an open source workbench for advanced computer aided translation. 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Optimized MT Online Learning in Computer Assisted Translation

In this paper we propose a cascading framework for optimizing online learning in machine translation for computer assisted translation scenario. With the use of online learning, one introduces several hyper parameters associated with the learning algorithm. Number of iterations of online learning can affect the quality of translation as well. We discuss these issues and propose a few approaches that can be used to optimize the hyper parameters and also to find the number of iterations required for online learning. We experimentally show that using optimal number of iterations in online learning proves to be useful and we get consistent improvement against baseline results

Multimodal Interactive Transcription of Handwritten Text Images

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Aprendizaje online de los pesos del modelo log-lineal en traducción automática interactiva

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