Learning when to skim and when to read

Alexander Rosenberg Johansen; Richard Socher

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arXiv · 1712.05483

Learning when to skim and when to read

Alexander Rosenberg Johansen·Richard Socher

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Abstract

Many recent advances in deep learning for natural language processing have come at increasing computational cost, but the power of these state-of-the-art models is not needed for every example in a dataset. We demonstrate two approaches to reducing unnecessary computation in cases where a fast but weak baseline classier and a stronger, slower model are both available. Applying an AUC-based metric to the task of sentiment classification, we find significant efficiency gains with both a probability-threshold method for reducing computational cost and one that uses a secondary decision network.

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Alexander Rosenberg Johansen, Richard Socher. 2017-12-15. Learning when to skim and when to read. https://arxiv.org/abs/1712.05483

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