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• W3146244013 abstract "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." @default.
• W3146244013 created "2021-04-13" @default.
• W3146244013 creator A5066002127 @default.
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• W3146244013 date "2021-04-05" @default.
• W3146244013 modified "2023-09-25" @default.
• W3146244013 title "Rethinking Perturbations in Encoder-Decoders for Fast Training" @default.
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• W3146244013 doi "https://doi.org/10.48550/arxiv.2104.01853" @default.
• W3146244013 hasPublicationYear "2021" @default.
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