Deep Adversarial Learning for NLP

William Yang Wang, Sameer Singh, Jiwei Li


Abstract
Adversarial learning is a game-theoretic learning paradigm, which has achieved huge successes in the field of Computer Vision recently. Adversarial learning is also a general framework that enables a variety of learning models, including the popular Generative Adversarial Networks (GANs). Due to the discrete nature of language, designing adversarial learning models is still challenging for NLP problems. In this tutorial, we provide a gentle introduction to the foundation of deep adversarial learning, as well as some practical problem formulations and solutions in NLP. We describe recent advances in deep adversarial learning for NLP, with a special focus on generation, adversarial examples & rules, and dialogue. We provide an overview of the research area, categorize different types of adversarial learning models, and discuss pros and cons, aiming at providing some practical perspectives on the future of adversarial learning for solving real-world NLP problems.
Anthology ID:
N19-5001
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Tutorials
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Editors:
Anoop Sarkar, Michael Strube
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–5
Language:
URL:
https://aclanthology.org/N19-5001
DOI:
10.18653/v1/N19-5001
Bibkey:
Cite (ACL):
William Yang Wang, Sameer Singh, and Jiwei Li. 2019. Deep Adversarial Learning for NLP. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Tutorials, pages 1–5, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
Deep Adversarial Learning for NLP (Wang et al., NAACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/N19-5001.pdf
Presentation:
 N19-5001.Presentation.pdf