Papers with catastrophically forgetting
Continual Learning Long Short Term Memory (2020.findings-emnlp)
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| Challenge: | Existing approaches to prevent catastrophic forgetting in neural networks are based on the stability-plasticity dilemma, but only a limited size of old data is available. |
| Approach: | They propose a Continual Learning Long Short Term Memory cell in Recurrent Neural Network (RNN) that considers the state of each individual task's output gates and the correlation of the states between tasks. |
| Outcome: | The proposed method significantly improves on spoken language understanding tasks over state-of-the-art approaches. |
Continual Machine Reading Comprehension via Uncertainty-aware Fixed Memory and Adversarial Domain Adaptation (2022.findings-naacl)
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| Challenge: | Existing stationary-trained MRC systems are usually trained with in-domain data but are applied to new domain data. |
| Approach: | They propose a continual machine reading comprehension model with uncertainty-aware fixed memory and adversarial domain adaptation that keeps a stable understanding by learning both memory and new domain data. |
| Outcome: | The proposed model is superior to strong baselines and has a substantial incremental learning ability without catastrophically forgetting under two different continual MRC settings. |