Towards Automated Error Analysis: Learning to Characterize Errors (2022)
Tong Gao, Shivang Singh, Raymond J. Mooney
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 features. We apply our approach to VilBERT, for Visual Question Answering, and RoBERTa, for Common Sense Question Answering. Our system learns interpretable rules that provide insights into systemic errors these systems make on the given tasks. Using these insights, we are also able to “close the loop” and modestly improve performance of these systems.
PDF, Arxiv
Short version appears in the 19th International Florida Artificial Intelligence Research Society Conference (FLAIRS) (2022).

Tong Gao Masters Alumni gaotong [at] utexas edu
Raymond J. Mooney Faculty mooney [at] cs utexas edu