How transfer learning impacts linguistic knowledge in deep NLP models? (2021.findings-acl)
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| Challenge: | Several researchers have shown that deep NLP models learn non-trivial amount of linguistic knowledge, captured at different layers of the model. |
| Approach: | They propose to fine-tune pre-trained models towards downstream NLP tasks to capture linguistic knowledge. |
| Outcome: | The proposed model is adapted to GLUE tasks and retains linguistic information in the network while forgetting it. |
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