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Counterfactual Generative Smoothing for Imbalanced Natural Language Classification
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Cited 3 time in Scopus
- Authors
- Issue Date
- 2021-10
- Publisher
- Association for Computing Machinery
- Citation
- International Conference on Information and Knowledge Management, Proceedings, pp.3058-3062
- Abstract
- © 2021 ACM.Classification datasets are often biased in observations, leaving onlya few observations for minority classes. Our key contribution is de-tecting and reducing Under-represented (U-) and Over-represented(O-) artifacts from dataset imbalance, by proposing a Counterfac-tual Generative Smoothing approach on both feature-space anddata-space, namely CGS_f and CGS_d. Our technical contribution issmoothing majority and minority observations, by sampling a ma-jority seed and transferring to minority. Our proposed approachesnot only outperform state-of-the-arts in both synthetic and real-lifedatasets, they effectively reduce both artifact types.
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