@inproceedings{wang-etal-2019-deep,
title = "Deep Adversarial Learning for {NLP}",
author = "Wang, William Yang and
Singh, Sameer and
Li, Jiwei",
editor = "Sarkar, Anoop and
Strube, Michael",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Tutorials",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-5001",
doi = "10.18653/v1/N19-5001",
pages = "1--5",
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.",
}
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%0 Conference Proceedings
%T Deep Adversarial Learning for NLP
%A Wang, William Yang
%A Singh, Sameer
%A Li, Jiwei
%Y Sarkar, Anoop
%Y Strube, Michael
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Tutorials
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F wang-etal-2019-deep
%X 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.
%R 10.18653/v1/N19-5001
%U https://aclanthology.org/N19-5001
%U https://doi.org/10.18653/v1/N19-5001
%P 1-5
Markdown (Informal)
[Deep Adversarial Learning for NLP](https://aclanthology.org/N19-5001) (Wang et al., NAACL 2019)
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.