Benchmarking Popular Classification Models' Robustness to Random and Targeted Corruptions

Text classification models, especially neural networks based models, have reached very high accuracy on many popular benchmark datasets. Yet, such models when deployed in real world applications, tend to perform badly. The primary reason is that these models are not tested against sufficient real wo...

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Veröffentlicht in:	arXiv.org 2020-01
Hauptverfasser:	Desai, Utkarsh, Tamilselvam, Srikanth, Kaur, Jassimran, Mani, Senthil, Khare, Shreya
Format:	Artikel
Sprache:	eng
Schlagworte:	Benchmarks Classification Datasets Model accuracy Model testing Neural networks
Online-Zugang:	Volltext
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