Chinese-Urdu neural machine translation interacting POS sequence prediction in Urdu language
At present,many research teams have conducted in-depth research on minority language machine translation for South and Southeast Asia.However,as the official language of Pakistan,Urdu has limited data resources and a significant gap from Chinese,resulting in a lack of targeted research on Chinese-Urdu machine translation methods.To address this issue,this paper proposes a Chinese-Urdu neural machine translation model based on Transformer and incorporating Urdu part-of-speech sequence prediction.Firstly,Transformer is used to predict the part-of-speech sequence of the target language Urdu.Then,the translation model's prediction results are combined with the part-of-speech sequence prediction model's results to jointly predict the final translation,thereby integrating language knowledge into the translation model.Experimental results on a small-scale Chinese-Urdu dataset show that the proposed method has a BLEU score of 0.13 higher than the baseline model on the dataset,achieving sig-nificant improvement.
Transformerneural machine translationUrdupart of speech sequence
万方数据
维普
