Open Access
ARTICLE
Interpreting Randomly Wired Graph Models for Chinese NER
Jie Chen1, Jiabao Xu1, Xuefeng Xi1,*, Zhiming Cui1, Victor S. Sheng2
1
The School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, China
2
The School of Computer Science, Texas Tech University, Texas, USA
* Corresponding Author: Xuefeng Xi. Email:
(This article belongs to the Special Issue: Advanced Intelligent Decision and Intelligent Control with Applications in Smart City)
Computer Modeling in Engineering & Sciences 2023, 134(1), 747-761. https://doi.org/10.32604/cmes.2022.020771
Received 10 December 2021; Accepted 25 February 2022; Issue published 24 August 2022
Abstract
Interpreting deep neural networks is of great importance to understand and verify deep models for natural language
processing (NLP) tasks. However, most existing approaches only focus on improving the performance of models
but ignore their interpretability. In this work, we propose a Randomly Wired Graph Neural Network (RWGNN)
by using graph to model the structure of Neural Network, which could solve two major problems (word-boundary
ambiguity and polysemy) of Chinese NER. Besides, we develop a pipeline to explain the RWGNN by using Saliency
Map and Adversarial Attacks. Experimental results demonstrate that our approach can identify meaningful and
reasonable interpretations for hidden states of RWGNN.
Keywords
Cite This Article
Chen, J., Xu, J., Xi, X., Cui, Z., Sheng, V. S. (2023). Interpreting Randomly Wired Graph Models for Chinese NER.
CMES-Computer Modeling in Engineering & Sciences, 134(1), 747–761. https://doi.org/10.32604/cmes.2022.020771