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arXiv · 1805.04680

AdvEntuRe: Adversarial Training for Textual Entailment with Knowledge-Guided Examples

Dongyeop Kang·Tushar Khot·Ashish Sabharwal·Eduard Hovy
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Abstract

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%.

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Dongyeop Kang, Tushar Khot, Ashish Sabharwal, Eduard Hovy. 2018-05-12. AdvEntuRe: Adversarial Training for Textual Entailment with Knowledge-Guided Examples. https://arxiv.org/abs/1805.04680

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