On the Nature of BERT: Correlating Fine-Tuning and Linguistic Competence (2022.coling-1)
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| Challenge: | Several studies on the interpretation of Neural Language Models (NLMs) focus on the linguistic generalization abilities of pre-trained models, but little attention is paid to how the linguistic knowledge of the models changes during fine-tuning. |
| Approach: | They propose to examine whether a wide range of linguistic phenomena are forgotten during fine-tuning and whether it is possible to predict the fine- tuned accuracy solely relying on the assessed linguistic competence. |
| Outcome: | The proposed model can predict the evolution of written language competence of native language learners based on the assessed linguistic competence. |
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Language Models as Knowledge Bases? (D19-1)
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Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, Alexander Miller
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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. |
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| Challenge: | A study of multilingual fine-tuning yields better performance on downstream NLP applications . low resource languages such as Oriya and Punjabi are found to be the largest beneficiaries of multi-lingual fine tuning. |
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Recall and Learn: Fine-tuning Deep Pretrained Language Models with Less Forgetting (2020.emnlp-main)
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| Challenge: | Existing methods to fine-tune deep pretrained language models face catastrophic forgetting problems. |
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