Papers with uniform distribution
Simple and effective data augmentation for compositional generalization (2024.naacl-long)
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| Challenge: | Compositional generalization is the ability of a system to correctly predict the meaning of complex sentences when trained on simpler sentences. |
| Approach: | They propose to use data augmentation methods to generate additional training data by sampling from an augmentation distribution to generalize to the out-of-distribution test data. |
| Outcome: | The proposed method outperforms existing methods that sampled from the training distribution and outperformed existing methods. |