Issue #35 – Text Repair Model for Neural Machine Translation

02 May19

Issue #35 – Text Repair Model for Neural Machine Translation

Author: Dr. Patrik Lambert, Machine Translation Scientist @ Iconic

Neural machine translation engines produce systematic errors which are not always easy to detect and correct in an end-to-end framework with millions of hidden parameters. One potential way to resolve these issues is doing so after the fact – correcting the errors by post-processing the output with an automatic post-editing (APE) step. This week we take a look at a neural approach to the APE problem.


An APE engine is usually trained on machine translated data and a reference, which is ideally the corresponding manually post-edited data. Since manual post-editing is costly, a parallel corpus can be used to train the APE engine, by translating its source side and comparing the translation to the target side. Freitag et al. (2019) propose a neural APE engine trained on monolingual data (provided source-target and target-source MT models are available) via round-trip translation. The data is translated from the source language to the target language and then translated back to the source language. A neural MT engine is then trained on the parallel corpus formed by
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