@inproceedings{xu-etal-2019-lexicalat,
title = "{L}exical{AT}: Lexical-Based Adversarial Reinforcement Training for Robust Sentiment Classification",
author = "Xu, Jingjing and
Zhao, Liang and
Yan, Hanqi and
Zeng, Qi and
Liang, Yun and
Sun, Xu",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1554",
doi = "10.18653/v1/D19-1554",
pages = "5518--5527",
abstract = "Recent work has shown that current text classification models are fragile and sensitive to simple perturbations. In this work, we propose a novel adversarial training approach, LexicalAT, to improve the robustness of current classification models. The proposed approach consists of a generator and a classifier. The generator learns to generate examples to attack the classifier while the classifier learns to defend these attacks. Considering the diversity of attacks, the generator uses a large-scale lexical knowledge base, WordNet, to generate attacking examples by replacing some words in training examples with their synonyms (e.g., sad and unhappy), neighbor words (e.g., fox and wolf), or super-superior words (e.g., chair and armchair). Due to the discrete generation step in the generator, we use policy gradient, a reinforcement learning approach, to train the two modules. Experiments show LexicalAT outperforms strong baselines and reduces test errors on various neural networks, including CNN, RNN, and BERT.",
}
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%0 Conference Proceedings
%T LexicalAT: Lexical-Based Adversarial Reinforcement Training for Robust Sentiment Classification
%A Xu, Jingjing
%A Zhao, Liang
%A Yan, Hanqi
%A Zeng, Qi
%A Liang, Yun
%A Sun, Xu
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F xu-etal-2019-lexicalat
%X Recent work has shown that current text classification models are fragile and sensitive to simple perturbations. In this work, we propose a novel adversarial training approach, LexicalAT, to improve the robustness of current classification models. The proposed approach consists of a generator and a classifier. The generator learns to generate examples to attack the classifier while the classifier learns to defend these attacks. Considering the diversity of attacks, the generator uses a large-scale lexical knowledge base, WordNet, to generate attacking examples by replacing some words in training examples with their synonyms (e.g., sad and unhappy), neighbor words (e.g., fox and wolf), or super-superior words (e.g., chair and armchair). Due to the discrete generation step in the generator, we use policy gradient, a reinforcement learning approach, to train the two modules. Experiments show LexicalAT outperforms strong baselines and reduces test errors on various neural networks, including CNN, RNN, and BERT.
%R 10.18653/v1/D19-1554
%U https://aclanthology.org/D19-1554
%U https://doi.org/10.18653/v1/D19-1554
%P 5518-5527
Markdown (Informal)
[LexicalAT: Lexical-Based Adversarial Reinforcement Training for Robust Sentiment Classification](https://aclanthology.org/D19-1554) (Xu et al., EMNLP-IJCNLP 2019)
ACL