Custom translator - Model adjustment after training

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I've used three parallel sentence files to train my custom translator model. No dictionary files and no tuning files too. After training is finished and I've checked test results, I want to make some adjustments in the model. And here are several questions:

  1. Is it possible to tune the model after training? Am I right that the model can't be changed and the only way is to train a new model?
  2. The best approach to adjusting the model is to use tune files. Is it correct?
  3. There is no way to see an autogenerated tune file, so I have to provide my own tuning file for a more manageable tuning process. Is it so?
  4. Could you please describe how the tuning file is generated, when I have 3 sentence files with different amount of sentences, which is: 55k, 24k and 58k lines. Are all tuning sentences is from the first file or from all three files proportionally to their size? Which logic is used?
microsoft-translator
1 Answers

I wish there were more authoritative answers on this, I'll share what I know as a fellow user.

What Microsoft Custom Translator calls "tuning data" is what is normally known as a validation set. It's just a way to avoid overfitting.

  1. Is it possible to tune the model after training? Am I right that the model can't be changed and the only way is to train a new model?

Yes, with Microsoft Custom Translator you can only train a model based on the generic category you have selected for the project.

(With Google AutoML technically you can choose to train a new model based on one of your previous custom models. However, it's also not usable without some trial and error.)

  1. The best approach to adjusting the model is to use tune files. Is it correct?

It's hard to make a definitive statement on this. The training set also has an effect. A good validation set on top of a bad training set won't get us good results.

  1. There is no way to see an autogenerated tune file, so I have to provide my own tuning file for a more manageable tuning process. Is it so?

Yes, it seems to me that if you let it decide how to split the training set into the training set, tuning set and test set, you can only download the training set and the test set.

Maybe neither includes the tuning set, so theoretically you can diff them. But that doesn't solve the problem of the split being different between different models.

  1. ... Which logic is used?

Good question.

user_44868600
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