Papers by Dengji Guo
Prediction Difference Regularization against Perturbation for Neural Machine Translation (2022.acl-long)
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| Challenge: | Existing methods for regularizing input perturbation are limited by under-fitting of training data. |
| Approach: | They propose a method that can reduce over-fitting and under-fitting at the same time. |
| Outcome: | The proposed method can reduce over-fitting and under-fitturing while making the model less sensitive to small input changes and more robust to under-perturbed training data. |
Guiding Teacher Forcing with Seer Forcing for Neural Machine Translation (2021.acl-long)
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| Challenge: | Neural machine translation models are usually based on attention-based encoder-decoder frameworks. |
| Approach: | They introduce a seer decoder into the encoder-decoder framework during training . they force the conventional decoded decodes to simulate the behavior of the seer . |
| Outcome: | The proposed method outperforms baselines on Chinese, English and German translation tasks. |