Papers with auxiliary language model objective

1 papers
An Embarrassingly Simple Approach for Transfer Learning from Pretrained Language Models (N19-1)

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Challenge: Existing transfer learning methods employ language models pretrained on large generic corpora, but results come at a high computational cost and require task-specific architectures.
Approach: They propose a transfer learning approach that combine a task-specific optimization function with an auxiliary language model objective, which is adjusted during the training process.
Outcome: The proposed method surpasses well established transfer learning methods with greater level of complexity on a variety of affective and text classification tasks surpassing well established methods with higher level of difficulty.

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