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Targeted Adversarial Training for Natural Language Understanding

التدريب الخصم المستهدف لفهم اللغة الطبيعية

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 Publication date 2021
and research's language is English
 Created by Shamra Editor




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We present a simple yet effective Targeted Adversarial Training (TAT) algorithm to improve adversarial training for natural language understanding. The key idea is to introspect current mistakes and prioritize adversarial training steps to where the model errs the most. Experiments show that TAT can significantly improve accuracy over standard adversarial training on GLUE and attain new state-of-the-art zero-shot results on XNLI. Our code will be released upon acceptance of the paper.



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