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Training Neural Machine Translation to Apply Terminology Constraints

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Table 1: The two alternative ways used to generate source-target training data, including target terms in the source andfactors indicating source words (0), source terms (1), and tar-get terms (2).
Table 2: Term usage percentage and BLEU scores of sys-tems supplied with correct term entries, exactly matching thesource and the target
Table 4this test case is already more difficult for con-strained decoding as well as for train-by-replace,most likely due to the removal of the originalsource side content.by-append still performs better than the base-line, while constrained decoding shows

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