Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Rethinking Perturbations in Encoder-Decoders for Fast Training   We often use perturbations to regularize neural models. For neural
encoder-decoders, previous studies applied the scheduled sampling (Bengio et
al., 2015) and adversarial perturbations (Sato et al., 2019) as perturbations
but these methods require considerable computational time. Thus, this study
addresses the question of whether these approaches are efficient enough for
training time. We compare several perturbations in sequence-to-sequence
problems with respect to computational time. Experimental results show that the
simple techniques such as word dropout (Gal and Ghahramani, 2016) and random
replacement of input tokens achieve comparable (or better) scores to the
recently proposed perturbations, even though these simple methods are faster.
Our code is publicly available at
https://github.com/takase/rethink_perturbations.

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The categories of this paper are: patt-sol
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The categories of this paper are: ao-sci
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The categories of this paper are: nlin
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The categories of this paper are: quant-ph
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The categories of this paper are: solv-int
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The categories of this paper are: q-fin
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The categories of this paper are: hep-ex
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The categories of this paper are: bayes-an
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The categories of this paper are: funct-an
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The categories of this paper are: math
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The categories of this paper are: chao-dyn
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The categories of this paper are: plasm-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: math-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: comp-gas