Papers with static training
Overcoming Catastrophic Forgetting beyond Continual Learning: Balanced Training for Neural Machine Translation (2022.acl-long)
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| Challenge: | Neural networks tend to gradually forget the previously learned knowledge when learning multiple tasks sequentially from dynamic data distributions. |
| Approach: | They propose a method that iteratively provides complementary knowledge to student models by dynamically updating teacher models trained on specific data orders. |
| Outcome: | The proposed method improves on multiple machine translation tasks and improves performance over baseline systems. |
Dynamic Oracles for Top-Down and In-Order Shift-Reduce Constituent Parsing (D18-1)
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| Challenge: | Top-down and in-order shift-reduce constituent parsers are the most accurate known shift-reducing algorithms for constituent paring. |
| Approach: | They propose to use dynamic oracles to train two of the most accurate shift-reduce algorithms for constituent parsing. |
| Outcome: | The proposed top-down and in-order shift-reduce parsers improve on the WSJ benchmark. |