Issue #37 – Zero-shot Neural MT as Domain Adaptation
16 May19
Issue #37 – Zero-shot Neural MT as Domain Adaptation
Author: Dr. Patrik Lambert, Machine Translation Scientist @ Iconic
Zero-shot machine translation – a topic we first covered in Issue #6 – is the idea that you can have a single MT engine that can translate between multiple languages. Such multilingual Neural MT systems can be built by simply concatenating parallel sentence pairs in several language directions and only adding a token in the source side indicating to which language it should be translated. The system learns how to encode the input into a vector representation for several different languages, and how to generate the output conditioned on the encoder representation. This configuration enables zero-shot translation, that is the translation in a language direction not seen in training.
However, so far zero-shot translation has been much worse than translation via a pivot language. In this post, we take a look at a paper which analyses why zero-shot translation is not working and proposes effective solutions by considering a new source language as a new domain.
The missing ingredient
Arivazhagan et al. (2019) build a multilingual neural MT system from English into French and
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