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Amit Jindal

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AAAI Conference 2020 Short Paper

Leveraging BERT with Mixup for Sentence Classification (Student Abstract)

Amit Jindal
Dwaraknath Gnaneshwar
Ramit Sawhney
Rajiv Ratn Shah

Good generalization capability is an important quality of well-trained and robust neural networks. However, networks usually struggle when faced with samples outside the training distribution. Mixup is a technique that improves generalization, reduces memorization, and increases adversarial robustness. We apply a variant of Mixup called Manifold Mixup to the sentence classiﬁcation problem, and present the results along with an ablation study. Our methodology outperforms CNN, LSTM, and vanilla BERT models in generalization.

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