OpenNMT Forum

Domain Adaptation Techniques

Domain Adaptation can be useful when you want to train a Machine Translation model, but you have only too limited data.

There are several approaches of Domain Adaptation including:

  • Incremental Training / Re-training: So you have a big pre-trained model trained on a big corpus, and you continue training it with the new data from the small corpus.
  • Ensemble Decoding (of two models): You have two models and you use both models during translation.
  • Combining Training Data: You merge the two corpora and train one model on the whole combined data.
  • Data Weighting: You give higher weights for specialized segments over generic segments.

In this tutorial, I explained how to apply these techniques and the best practices:

If you have questions or suggestions, please let me know.

Kind regards,


Awesome, thank you @ymoslem !

Best regards


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