Towards Automated Error Analysis: Learning to Characterize Errors. (arXiv:2201.05017v1 [cs.CL])

Characterizing the patterns of errors that a system makes helps researchers
focus future development on increasing its accuracy and robustness. We propose
a novel form of "meta learning" that automatically learns interpretable rules
that characterize the types of errors that a system makes, and demonstrate
these rules' ability to help understand and improve two NLP systems. Our
approach works by collecting error cases on validation data, extracting
meta-features describing these samples, and finally learning rules that
characterize errors using these …

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