@Article{cmes.2022.020771, AUTHOR = {Jie Chen, Jiabao Xu, Xuefeng Xi, Zhiming Cui, Victor S. Sheng}, TITLE = {Interpreting Randomly Wired Graph Models for Chinese NER}, JOURNAL = {Computer Modeling in Engineering \& Sciences}, VOLUME = {134}, YEAR = {2023}, NUMBER = {1}, PAGES = {747--761}, URL = {http://www.techscience.com/CMES/v134n1/49443}, ISSN = {1526-1506}, 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.}, DOI = {10.32604/cmes.2022.020771} }