AdvEntuRe: Adversarial Training for Textual Entailment with Knowledge-Guided Examples
Dongyeop Kang, Tushar Khot, Ashish Sabharwal, Eduard H. Hovy · 2018
We consider the problem of learning textual entailment models with limited supervision (5K-10K training examples), and present two complementary approaches for it.First, we propose knowledge-guided adversarial example generators for incorporating large lexical resources in entailment models via only a handful of rule templates.Second, to make the entailment model-a discriminator-more robust, we propose the first GAN-style approach for training it using a natural language example generator that iteratively adjusts based on the discriminator's performance.We demonstrate effectiveness using two entailment datasets, where the proposed methods increase accuracy by 4.7% on SciTail and by 2.8% on a 1% training sub-sample of SNLI.Notably, even a single hand-written rule, negate, improves the accuracy on the negation examples in SNLI by 6.1%.P: The dog did not eat all of the chickens.H: The dog ate all of the chickens.S: entails (score 56:5%) P: The red box is in the blue box.H: The blue box is in the red box.