TL;DR: Today’s NLP models, for all their recent successes, have certain limitations. Case in point: they exhibit poor performance when processing multilingual code-mixed sentences (each containing multiple languages). Our new approach addresses this problem by constructing code-mixed inputs designed to degrade (or “attack”) the model, exposing the limitations

Morpheus exposes the potential allocative harms of popular pretrained NLP models by simulating inflectional variation. We propose adversarial fine-tuning for mitigating the effects of training only on error-free Standard English data.

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