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

arXiv: https://arxiv.org/abs/1805.04680 · HTML: https://mlsanity.com/paper/1805.04680/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 May 2018.

## Built on by (most cited first, 15 of 15)

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- [ExBERT: An External Knowledge Enhanced BERT for Natural Language Inference](https://mlsanity.com/paper/2108.01589/) (2021)
- [NatLogAttack: A Framework for Attacking Natural Language Inference Models with Natural Logic](https://mlsanity.com/paper/2307.02849/) (2023)
- [Linguistic Knowledge in Data Augmentation for Natural Language Processing: An Example on Chinese Question Matching](https://mlsanity.com/paper/2111.14709/) (2021)
- ['John ate 5 apples' != 'John ate some apples': Self-Supervised Paraphrase Quality Detection for Algebraic Word Problems](https://mlsanity.com/paper/2206.08263/) (2022)
- [Probabilistic Linguistic Knowledge and Token-level Text Augmentation](https://mlsanity.com/paper/2306.16644/) (2023)
- [READ: Improving Relation Extraction from an ADversarial Perspective](https://mlsanity.com/paper/2404.02931/) (2024)
- [Evaluating Multi-Dimensional Generalization of Large Language Models in Temporal Extraction Tasks](https://mlsanity.com/paper/2610.02549/) (2026)

## Abstract (arXiv)

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%.