Regularization techniques for fine-tuning in neural machine translation
Regularization techniques for fine-tuning in neural machine translation
We investigate techniques for supervised domain adaptation for neural machine translation where an existing model trained on a large out-of-domain dataset is adapted to a small in-domain dataset. In this scenario, overfitting is a major challenge. We investigate a number of techniques to reduce overfitting and improve transfer learning, including …