Papers with naive training
Improved Zero-shot Neural Machine Translation via Ignoring Spurious Correlations (P19-1)
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| Challenge: | Existing approaches to train a multilingual NMT model for low-resource languages are lacking in terms of number of supervised examples. |
| Approach: | They propose to use decoder pre-training and back-translation to solve the degeneracy problem by analyzing spurious correlations between source and decoded sentences. |
| Outcome: | The proposed methods show significant improvement over the pivot-based approach on three challenging multilingual datasets. |
Empower Nested Boolean Logic via Self-Supervised Curriculum Learning (2023.emnlp-main)
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| Challenge: | a new self-supervised learning method allows language models to generalize to much harder and longer-hop logic. |
| Approach: | They propose a self-supervised learning method to empower language models with boolean logic . they augment training data with nested booles and program training from simpler to harder ones . |
| Outcome: | The proposed method allows language models to generalize to much harder and longer-hop logic, which can hardly be learned through naive training. |