Papers with continual learning algorithms
Learn Continually, Generalize Rapidly: Lifelong Knowledge Accumulation for Few-shot Learning (2021.findings-emnlp)
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| Challenge: | Existing models that pursue rapid generalization to new tasks are mostly trained in a single shot on fixed datasets, unable to dynamically expand their knowledge. |
| Approach: | They propose a new learning setup that assumes a model learns from a sequence of diverse NLP tasks arriving sequentially, accumulating knowledge for improved generalization to new tasks. |
| Outcome: | The proposed learning setup improves generalization ability while retaining performance on the tasks learned earlier. |
Lifelong Pretraining: Continually Adapting Language Models to Emerging Corpora (2022.naacl-main)
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| Challenge: | Pretrained language models are typically learned over a large, static corpus and fine-tuned for various downstream tasks. |
| Approach: | They propose to continuously update a pretrained language model to adapt to emerging data and to keep track of the model's performance. |
| Outcome: | The proposed model can adapt to new corpora while retaining knowledge in earlier domains. |
Visually Grounded Continual Language Learning with Selective Specialization (2023.findings-emnlp)
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| Challenge: | Existing studies on the role of model components in learning specialized or generalizable representations are lacking. |
| Approach: | They propose to analyze selection strategies for visually grounded continual language learning using two diagnostic datasets. |
| Outcome: | The proposed models outperform existing models and provide enough control and flexibility for a thorough model analysis. |