Residual Learning of Neural Text Generation with n-gram Language Model (2022.findings-emnlp)
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| Challenge: | Experimental results show that n-gram models can achieve satisfactory performance on a large proportion of testing cases. |
| Approach: | They propose to learn a neural LM that fits the residual between an n-gram LM and the real-data distribution. |
| Outcome: | The proposed model achieves additional performance gains over popular standalone models on three typical language tasks. |
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