Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Low Resource Text Classification with ULMFit and Backtranslation   In computer vision, virtually every state-of-the-art deep learning system is
trained with data augmentation. In text classification, however, data
augmentation is less widely practiced because it must be performed before
training and risks introducing label noise. We augment the IMDB movie reviews
dataset with examples generated by two families of techniques: random token
perturbations introduced by Wei and Zou [2019] and backtranslation --
translating to a second language then back to English. In low resource
environments, backtranslation generates significant improvement on top of the
state of-the-art ULMFit model. A ULMFit model pretrained on wikitext103 and
then fine-tuned on only 50 IMDB examples and 500 synthetic examples generated
by backtranslation achieves 80.6% accuracy, an 8.1% improvement over the
augmentation-free baseline with only 9 minutes of additional training time.
Random token perturbations do not yield any improvements but incur equivalent
computational cost. The benefits of training with backtranslated examples
decreases with the size of the available training data. On the full dataset,
neither augmentation technique improves upon ULMFit's state of the art
performance. We address this by using backtranslations as a form of test time
augmentation as well as ensembling ULMFit with other models, and achieve small
improvements.

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The categories of this paper are: nucl-th
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The categories of this paper are: solv-int
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The categories of this paper are: plasm-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: supr-con
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The categories of this paper are: q-alg
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The categories of this paper are: q-fin
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The categories of this paper are: hep-th
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The categories of this paper are: bayes-an
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The categories of this paper are: hep-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: patt-sol
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The categories of this paper are: eess
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The categories of this paper are: mtrl-th
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The categories of this paper are: acc-phys