Papers with discrete generation step

1 papers
LexicalAT: Lexical-Based Adversarial Reinforcement Training for Robust Sentiment Classification (D19-1)

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Challenge: Existing text classification models are fragile and sensitive to simple perturbations.
Approach: They propose a generator-classifier adversarial training approach to improve classification models . they use a large-scale lexical knowledge base to generate attacking examples .
Outcome: The proposed approach outperforms strong baselines and reduces test errors on neural networks.

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