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• W2938432841 abstract "We propose a simple data augmentation protocol aimed at providing a compositional inductive bias in conditional and unconditional sequence models. Under this protocol, synthetic training examples are constructed by taking real training examples and replacing (possibly discontinuous) fragments with other fragments that appear in at least one similar environment. The protocol is model-agnostic and useful for a variety of tasks. Applied to neural sequence-to-sequence models, it reduces error rate by as much as 87% on diagnostic tasks from the SCAN dataset and 16% on a semantic parsing task. Applied to n-gram language models, it reduces perplexity by roughly 1% on small corpora in several languages." @default.
• W2938432841 created "2019-04-25" @default.
• W2938432841 creator A5070829652 @default.
• W2938432841 date "2019-04-21" @default.
• W2938432841 modified "2023-09-27" @default.
• W2938432841 title "Good-Enough Compositional Data Augmentation" @default.
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• W2938432841 hasPublicationYear "2019" @default.
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