Robust Text Classification: Analyzing Prototype-Based Networks
Zhivar Sourati Darshan Deshpande Filip Ilievski Kiril Gashteovski Sascha Saralajew
Downstream applications often require text classification models to be
accurate, robust, and interpretable. While the accuracy of the stateof-the-art
language models approximates human performance, they are not designed to be
interpretable and often exhibit a drop in performance on noisy data. The family
of PrototypeBased Networks (PBNs) that classify examples based on their
similarity to prototypical examples of a class (prototypes) is natively
interpretable and shown to be robust to noise, which enabled its wide usage for
computer vision tasks. In this paper, we study whether the robustness
properties of PBNs transfer to text classification tasks. We design a modular
and comprehensive framework for studying PBNs, which includes different
backbone architectures, backbone sizes, and objective functions. Our evaluation
protocol assesses the robustness of models against character-, word-, and
sentence-level perturbations. Our experiments on three benchmarks show that the
robustness of PBNs transfers to NLP classification tasks facing realistic
perturbations. Moreover, the robustness of PBNs is supported mostly by the
objective function that keeps prototypes interpretable, while the robustness
superiority of PBNs over vanilla models becomes more salient as datasets get
more complex.