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Segment-based interactive-predictive machine translation

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Machine Translation

Abstract

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.

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Notes

  1. http://www.statmt.org/wmt14/medical-task/.

  2. https://wit3.fbk.eu/mt.php?release=2013-01.

  3. http://www.statmt.org/wmt15/translation-task.html.

  4. The partition selected as development was news-test2013.

  5. One mouse action is enough for selecting or deleting a one-word segment: the user would simply click on the word.

  6. Tested on a machine with an Intel i5 CPU at 3.1 GHz.

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Acknowledgements

The research leading to these results has received funding from the Ministerio de Economía 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).

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  1. Pattern Recognition and Human Language Technology Research Center, Universitat Politècnica de València, Camino de Vera s/n, 46022, Valencia, Spain

    Miguel Domingo, Álvaro Peris & Francisco Casacuberta

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  1. Miguel Domingo
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  2. Álvaro Peris
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  3. Francisco Casacuberta
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Correspondence to Miguel Domingo.

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Domingo, M., Peris, Á. & Casacuberta, F. Segment-based interactive-predictive machine translation. Machine Translation 31, 163–185 (2017). https://doi.org/10.1007/s10590-017-9213-3

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