Papers with unsupervised instance-wise training signals
Rethinking Denoised Auto-Encoding in Language Pre-Training (2021.emnlp-main)
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| Challenge: | Pre-trained models such as BERT have achieved success in learning sequence representations, but they tend to learn representations that are covariant with the noise of pre-training. |
| Approach: | They propose to train self-trained models to learn noise invariant sequence representations . they encourage consistency between original sequence and corrupted version via unsupervised instance-wise training signals. |
| Outcome: | The proposed model improves on 11 natural language understanding and cross-modal tasks and achieves 0.6% gain on GLUE benchmarks and 0.8% increment on NLVR2 . |