Papers with model-independent data augmentation approach

1 papers
Conditional set generation using Seq2seq models (2022.emnlp-main)

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Challenge: Several NLP tasks are instances of set generation.
Approach: They propose a model-independent data augmentation approach that enlarges the model with the signals of order-invariance and cardinality.
Outcome: The proposed method improves performance on four benchmark datasets with no additional annotations.

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