Papers with adversarial sub models
Improving Mongolian-Chinese Neural Machine Translation with Morphological Noise (P19-2)
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| Challenge: | Existing models for Mongolian-Chinese translation are based on recurrent, convolutional neural networks or completely eliminate recurrence connections. |
| Approach: | They propose a adversarial training model to alleviate the UNK problem in Mongolian-Chinese machine translation by adding a screener to the model to emphasize the added Mongolian morphological noise. |
| Outcome: | The proposed model reduces training time and improves accuracy in Mongolian-Chinese translation tasks. |
Improving Neural Machine Translation with Conditional Sequence Generative Adversarial Nets (N18-1)
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| Challenge: | Experimental results show that the proposed model consistently outperforms the traditional RNNSearch and the newly emerged state-of-the-art Transformer on English-German and Chinese-English translation tasks. |
| Approach: | They propose an approach for applying GANs to NMT by building a conditional sequence generative adversarial net with two adversarials. |
| Outcome: | The proposed model outperforms the existing RNNSearch and Transformer on English-German and Chinese-English translation tasks. |