Revisiting Adversarial Autoencoder for Unsupervised Word Translation with Cycle Consistency and...

时间 2021-01-02

标签 NLP 自然语言处理机器翻译无监督机器翻译栏目 Microsoft Office 繁體版

原文原文链接

2019NAACL的一篇文章，个人觉得很好的一篇文章，将去噪自编码器和回译应用到词对齐的过程中，应用比较灵活。文章链接： Revisiting Adversarial Autoencoder for Unsupervised Word Translation with Cycle Consistency and Improved Training 代码链接： https://ntunlpsg.g

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1. Improving Unsupervised Word-by-Word Translation with Language Model and Denoising Autoencoder
2. Unsupervised Bilingual Word Embedding Agreement for Unsupervised Neural Machine Translation
3. Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
4. Adversarial Autoencoder
5. CycleGAN:Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
6. CVPR：Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
7. Extract and Edit: An Alternative to Back-Translation for Unsupervised Neural Machine Translation
8. Unsupervised Image-to-Image Translation Networks---VAE+GAN+Cycle
9. Unsupervised Data Augmentation for Consistency Training
10. CrossNet: Latent Cross-Consistency for Unpaired Image Translation
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1. Improving Unsupervised Word-by-Word Translation with Language Model and Denoising Autoencoder
2. Unsupervised Bilingual Word Embedding Agreement for Unsupervised Neural Machine Translation
3. Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
4. Adversarial Autoencoder
5. CycleGAN:Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
6. CVPR：Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
7. Extract and Edit: An Alternative to Back-Translation for Unsupervised Neural Machine Translation
8. Unsupervised Image-to-Image Translation Networks---VAE+GAN+Cycle
9. Unsupervised Data Augmentation for Consistency Training
10. CrossNet: Latent Cross-Consistency for Unpaired Image Translation

>>更多相关文章<<