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- W4313171509 abstract "The transformer has improved slow training, the most criticized disadvantage, of RNNs and achieves fast parallelism by self-attention mechanism. Also, the transformer can be enhanced to a great depth to fully exploit the characteristics of the deep neural network model and improve the model’s accuracy, which is widely used in machine translation. Deep models, however, have the inevitable disadvantage of being prone to overfitting. We consider introducing the adversarial training method in the translation model to improve the robustness of the model through regularization to alleviate the overfitting problem. We conducted analytical experiments on the English-Chinese translation dataset which shows that the adversarial training approach can improve translation quality." @default.
- W4313171509 created "2023-01-06" @default.
- W4313171509 creator A5007609586 @default.
- W4313171509 creator A5026821441 @default.
- W4313171509 date "2022-10-20" @default.
- W4313171509 modified "2023-09-27" @default.
- W4313171509 title "Improving English to Chinese machine translation performance via adversarial training" @default.
- W4313171509 doi "https://doi.org/10.1117/12.2656585" @default.
- W4313171509 hasPublicationYear "2022" @default.
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