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        <title>AdvEntuRe: Adversarial Training for Textual Entailment with Knowledge-Guided Examples</title>
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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 to the discriminator’s weaknesses. 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% sub-sample of SNLI. Notably, even a single hand-written rule, negate, improves the accuracy of negation examples in SNLI by 6.1%.</abstract>
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    <identifier type="doi">10.18653/v1/P18-1225</identifier>
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%0 Conference Proceedings
%T AdvEntuRe: Adversarial Training for Textual Entailment with Knowledge-Guided Examples
%A Kang, Dongyeop
%A Khot, Tushar
%A Sabharwal, Ashish
%A Hovy, Eduard
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F kang-etal-2018-adventure
%X 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 to the discriminator’s weaknesses. 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% sub-sample of SNLI. Notably, even a single hand-written rule, negate, improves the accuracy of negation examples in SNLI by 6.1%.
%R 10.18653/v1/P18-1225
%U https://aclanthology.org/P18-1225
%U https://doi.org/10.18653/v1/P18-1225
%P 2418-2428

Markdown (Informal)

[AdvEntuRe: Adversarial Training for Textual Entailment with Knowledge-Guided Examples](https://aclanthology.org/P18-1225) (Kang et al., ACL 2018)

• AdvEntuRe: Adversarial Training for Textual Entailment with Knowledge-Guided Examples (Kang et al., ACL 2018)

ACL

• Dongyeop Kang, Tushar Khot, Ashish Sabharwal, and Eduard Hovy. 2018. AdvEntuRe: Adversarial Training for Textual Entailment with Knowledge-Guided Examples. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2418–2428, Melbourne, Australia. Association for Computational Linguistics.