Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: MixUp Training Leads to Reduced Overfitting and Improved Calibration for
  the Transformer Architecture   MixUp is a computer vision data augmentation technique that uses convex
interpolations of input data and their labels to enhance model generalization
during training. However, the application of MixUp to the natural language
understanding (NLU) domain has been limited, due to the difficulty of
interpolating text directly in the input space. In this study, we propose MixUp
methods at the Input, Manifold, and sentence embedding levels for the
transformer architecture, and apply them to finetune the BERT model for a
diverse set of NLU tasks. We find that MixUp can improve model performance, as
well as reduce test loss and model calibration error by up to 50%.

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The categories of this paper are: solv-int
12
The categories of this paper are: nucl-th
18
The categories of this paper are: econ
7
The categories of this paper are: hep-th
0
The categories of this paper are: hep-ph
20
The categories of this paper are: acc-phys
28
The categories of this paper are: alg-geom
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The categories of this paper are: comp-gas
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The categories of this paper are: chao-dyn
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The categories of this paper are: patt-sol
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The categories of this paper are: dg-ga
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The categories of this paper are: hep-lat
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The categories of this paper are: nucl-ex
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The categories of this paper are: eess
14
The categories of this paper are: math-ph
36
The categories of this paper are: cmp-lg