Training Neural Machine Translation To Apply Terminology Constraints. (arXiv:1906.01105v1 [cs.CL])

This paper proposes a novel method to inject custom terminology into neural
machine translation at run time. Previous works have mainly proposed
modifications to the decoding algorithm in order to constrain the output to
include run-time-provided target terms. While being effective, these
constrained decoding methods add, however, significant computational overhead
to the inference step, and, as we show in this paper, can be brittle when
tested in realistic conditions. In this paper we approach the problem by
training a neural MT system to learn how to use custom terminology when
provided with the input. Comparative experiments show that our method is not
only more effective than a state-of-the-art implementation of constrained
decoding, but is also as fast as constraint-free decoding.

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