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Adversarial training has been shown effective at endowing the learnedrepresentations with stronger generalization ability. However, it typicallyrequires expensive computation to determine the direction of the injectedperturbations. In this paper, we introduce a set of simple yet effective dataaugmentation strategies dubbed cutoff, where part of the information within aninput sentence is erased to yield its restricted views (during the fine-tuningstage). Notably, this process relies merely on stochastic sampling and thusadds little computational overhead. A Jensen-Shannon Divergence consistencyloss is further utilized to incorporate these augmented samples into thetraining objective in a principled manner. To verify the effectiveness of theproposed strategies, we apply cutoff to both natural language understanding andgeneration problems. On the GLUE benchmark, it is demonstrated that cutoff, inspite of its simplicity, performs on par or better than several competitiveadversarial-based approaches. We further extend cutoff to machine translationand observe significant gains in BLEU scores (based upon the Transformer Basemodel). Moreover, cutoff consistently outperforms adversarial training andachieves state-of-the-art results on the IWSLT2014 German-English dataset.

A Simple but Tough-to-Beat Data Augmentation Approach for Natural Language Understanding and Generation