Abstract
Deep learning models have made incredible progress in tackling an assortment of natural language processing (NLP) issues. An ever-developing assortment of research, in any case, outlines the dependence of deep neural systems (DNNs) to ill-disposed models—inputs adjusted by acquainting little irritations with knowingly fooling an objective model into yielding mistaken outcomes. The powerlessness to aggressive models has gotten one of the fundamental obstacles blocking neural system organization into security basic conditions. This paper talks about the contemporary utilization of ill-disposed guides to thwart DNNs and presents an extensive audit of their utilization to improve the robustness of DNNs in NLP applications.
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Dinesh, P.M., Sujitha, V., Salma, C., Srijayapriya, B. (2021). A Review on Natural Language Processing: Back to Basics. In: Raj, J.S., Iliyasu, A.M., Bestak, R., Baig, Z.A. (eds) Innovative Data Communication Technologies and Application. Lecture Notes on Data Engineering and Communications Technologies, vol 59. Springer, Singapore. https://doi.org/10.1007/978-981-15-9651-3_54
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