Normal vs. Adversarial: Salience-based Analysis of Adversarial Samples for Relation Extraction

Author:

Li Luoqiu1,Chen Xiang1,Bi Zhen1,Xie Xin1,Deng Shumin1,Zhang Ningyu1,Tan Chuanqi2,Chen Mosha2,Chen Huajun1

Affiliation:

1. Zhejiang University, China

2. Alibaba Group, China

Funder

NSFC

Publisher

ACM

Reference48 articles.

1. Matching the Blanks: Distributional Similarity for Relation Learning

2. The elephant in the interpretability room: Why use attention as explanation when we have saliency methods?

3. Analysis Methods in Neural Language Processing: A Survey

4. Low-resource extraction with knowledge-aware pairwise prototype learning

5. Shumin Deng , Ningyu Zhang , Jiaojian Kang , Yichi Zhang , Wei Zhang , and Huajun Chen . 2020 . Meta-Learning with Dynamic-Memory-Based Prototypical Network for Few-Shot Event Detection. In WSDM ’20 : The Thirteenth ACM International Conference on Web Search and Data Mining , Houston, TX, USA , February 3-7, 2020, James Caverlee, Xia (Ben) Hu, Mounia Lalmas, and Wei Wang (Eds.). ACM, 151–159. https://doi.org/10.1145/3336191.3371796 10.1145/3336191.3371796 Shumin Deng, Ningyu Zhang, Jiaojian Kang, Yichi Zhang, Wei Zhang, and Huajun Chen. 2020. Meta-Learning with Dynamic-Memory-Based Prototypical Network for Few-Shot Event Detection. In WSDM ’20: The Thirteenth ACM International Conference on Web Search and Data Mining, Houston, TX, USA, February 3-7, 2020, James Caverlee, Xia (Ben) Hu, Mounia Lalmas, and Wei Wang (Eds.). ACM, 151–159. https://doi.org/10.1145/3336191.3371796

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