A Study on Chinese‐English Machine Translation Based on Transfer Learning and Neural Networks
Congli Li · Wireless Communications and Mobile Computing · 2022
The existing Chinese‐English machine translation has problems such as inaccurate word translation and difficult translation of long sentences. To this end, this paper proposes a new machine translation model based on bidirectional Chinese‐English translation incorporating translation knowledge and transfer learning, and the components of this model include a recurrent neural network‐based translation quality assessment model and a self‐focused network‐based model. The experimental results demonstrate that our method works better on the dataset of machine translation quality assessment task for Chinese‐English translation with more information, and the Pearson correlation coefficient of its quality assessment feature vector (such as word prediction vector representation) is higher.