Towards Automated Error Analysis: Learning to Characterize Errors
This work addresses the need for researchers to efficiently understand and improve system errors, though it appears incremental as it builds on existing meta-learning and error analysis techniques.
The paper tackles the problem of automating error analysis for machine learning systems by proposing a meta-learning approach that learns interpretable rules to characterize errors, and demonstrates its application to NLP systems like VilBERT and RoBERTa, resulting in modest performance improvements.
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.