Issue #7 – Terminology in Neural MT

30 Aug18

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

In many commercial MT use cases, being able to use custom terminology is a key requirement in terms of accuracy of the translation. The ability to guarantee the translation of specific input words and phrases is conveniently handled in Statistical MT (SMT) frameworks such as Moses. Because SMT is performed as a sequence of distinct steps, we can interject and specify directly how to translate certain words and phrases before the decoding step.

With the end-to-end concept of Neural MT systems, forcing terminology is not so readily supported. This obviously has critical implications for practical use, and thus there has been interest in this area, commonly known as constrained decoding. Let’s look at five approaches that have recently been proposed to introduce custom terminology in Neural MT.

Using Tags

A simple approach from Crego et al, 2016 consists of replacing terms with special tags which remain unchanged during translation and are replaced back in a post-processing step. This is done using attention weights or external alignments to solve ambiguous replacements. This approach can work well to translate specific entities,
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