Papers with Universal Language Model Fine-tuning

1 papers
Universal Language Model Fine-tuning for Text Classification (P18-1)

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Challenge: Existing approaches to computer vision require task-specific modifications and training from scratch.
Approach: They propose a method that can be applied to any task in NLP and propose to open-source it.
Outcome: The proposed method outperforms the state-of-the-art on six text classification tasks, reducing error by 18-24% on majority of datasets.

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