I trained a model by ~500,000 sentences (en-US -> es-ES), I compared the translated result between Microsoft Bing Translator and NMT server I deployed by the trained model.
For plain text, Bing Translator and NMT generate the similar translation, it looks good.
For strings with Tags (e.g: <tag0>, <tag1>..), Bing can keep the Tags as untranslated in translation in proper position what we expect. NMT can't return the similar result. See the screenshot for detail.
Note: In the attached screenshot, cf tags shown in SDL Trados Studio, I extract such tags and convert them by unique tag number tag0, tag1.. in order to get the source in plain text which can feed for NMT server. NMT Prepared Source: is the source in plain text I converted after Tokenization for feeding NMT server.
In order to keep the Tags same as Source when calling NMT Server, I attached a phrase table with below syntax and enable -phrase_table option, but it doesn't work.
My question is: How to let NMT keep such tags as same as Source? If the Train procedure can resolve this or any other alternative solution?