Papers by Tam Dang

1 papers
Variational Pretraining for Semi-supervised Text Classification (P19-1)

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Challenge: Empirically, we show the relative strength of VAMPIRE against computationally expensive contextual embeddings and other popular semi-supervised baselines under low resource settings.
Approach: They propose a lightweight framework for effective text classification when data and computing resources are limited.
Outcome: The proposed framework is compared with expensive contextual embeddings and semi-supervised baselines under low resource settings.

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