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        <title>DISK-CSV: Distilling Interpretable Semantic Knowledge with a Class Semantic Vector</title>
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    <abstract>Neural networks (NN) applied to natural language processing (NLP) are becoming deeper and more complex, making them increasingly difficult to understand and interpret. Even in applications of limited scope on fixed data, the creation of these complex “black-boxes” creates substantial challenges for debugging, understanding, and generalization. But rapid development in this field has now lead to building more straightforward and interpretable models. We propose a new technique (DISK-CSV) to distill knowledge concurrently from any neural network architecture for text classification, captured as a lightweight interpretable/explainable classifier. Across multiple datasets, our approach achieves better performance than the target black-box. In addition, our approach provides better explanations than existing techniques.</abstract>
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%0 Conference Proceedings
%T DISK-CSV: Distilling Interpretable Semantic Knowledge with a Class Semantic Vector
%A Bashier, Housam Khalifa
%A Kim, Mi-Young
%A Goebel, Randy
%Y Merlo, Paola
%Y Tiedemann, Jorg
%Y Tsarfaty, Reut
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F bashier-etal-2021-disk
%X Neural networks (NN) applied to natural language processing (NLP) are becoming deeper and more complex, making them increasingly difficult to understand and interpret. Even in applications of limited scope on fixed data, the creation of these complex “black-boxes” creates substantial challenges for debugging, understanding, and generalization. But rapid development in this field has now lead to building more straightforward and interpretable models. We propose a new technique (DISK-CSV) to distill knowledge concurrently from any neural network architecture for text classification, captured as a lightweight interpretable/explainable classifier. Across multiple datasets, our approach achieves better performance than the target black-box. In addition, our approach provides better explanations than existing techniques.
%R 10.18653/v1/2021.eacl-main.263
%U https://aclanthology.org/2021.eacl-main.263
%U https://doi.org/10.18653/v1/2021.eacl-main.263
%P 3021-3030

Markdown (Informal)

[DISK-CSV: Distilling Interpretable Semantic Knowledge with a Class Semantic Vector](https://aclanthology.org/2021.eacl-main.263) (Bashier et al., EACL 2021)

• DISK-CSV: Distilling Interpretable Semantic Knowledge with a Class Semantic Vector (Bashier et al., EACL 2021)

ACL

• Housam Khalifa Bashier, Mi-Young Kim, and Randy Goebel. 2021. DISK-CSV: Distilling Interpretable Semantic Knowledge with a Class Semantic Vector. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 3021–3030, Online. Association for Computational Linguistics.