E-VarM: Enhanced Variational Word Masks to Improve the Interpretability of Text Classification Models

Ling-ge, Chunming Hu, Guanghui Ma, Junshuang Wu, Junfan Chen, Jihong Liu, Hong Zhang, Wenyi Qin, Richong Zhang. E-VarM: Enhanced Variational Word Masks to Improve the Interpretability of Text Classification Models. In Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, YoungGyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na, editors, Proceedings of the 29th International Conference on Computational Linguistics, COLING 2022, Gyeongju, Republic of Korea, October 12-17, 2022. pages 1036-1050, International Committee on Computational Linguistics, 2022. [doi]

Abstract

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