Research on neural machine translation based on contrastive learning
Neural machine translation technology can help to break through the language barrier and strengthen cultural commu-nication.Aiming at the problems such as lack of parallel corpus and poor quality of neural machine translation,this paper com-bined three kinds of data augmentation methods to generate positive samples on unsupervised SimCSE comparative learning framework,so that the sentence embedment trained by this method could cover more semantic information.Then,using the com-parative learning method,the sentence embedding was pre-trained by mixing mono-language corpus.Finally,a small amount of parallel corpus was used to fine-tune the model.The experimental results showed that BLEU value was increased by 2.69.
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万方数据
