Papers by Celine Liang

1 papers
Generation-Distillation for Efficient Natural Language Understanding in Low-Data Settings (D19-61)

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Challenge: Recent research points to knowledge distillation as a potential solution for NLU tasks.
Approach: They propose a training approach that distills large finetuned LMs into a small network using unlabeled training examples.
Outcome: The proposed approach outperforms BERT training approaches while using 300 times fewer parameters.

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