Abstract: In recent years, neural machine translation model has become the mainstream model in the field of machine translation. How to learn translation knowledge quickly and accurately from a large amount of training data is a problem worthy of discussion. Different training samples have different degrees of difficulty. Some training samples are simpler and easy for model to learn, while others are more difficult and not easy for model to learn. The difficulty of the samples has a great influence on the convergence of the model, but the traditional neural machine translation model does not consider this difference in the training process. Therefore, this paper explores the influence of the difficulty of the samples on the training process of the neural machine translation model. Considering the sample difficulty for the neural machine translation mode, a dynamic learning method is proposed based on the idea of “curriculum learning”. The difficulty degree of the training samples is quantified from the aspects of the translation effect of the neural machine translation model and the sentence length of the training samples, respectively, then, two learning strategies are designed from-easy-to-difficult and from-difficult-to-easy to train the model. Finally, the translation effects of the model are compared. The experimental results show that both from-easy-to-difficult and from-difficult-to-easy dynamic learning methods can improve the translation effect of the neural machine translation model.

Key words: neural machine translation, curriculum learning, sample difficulty, dynamic learning