Understanding and Mitigating Classification Errors Through Interpretable
  Token Patterns
Michael A. Hedderich Jonas Fischer Dietrich Klakow Jilles Vreeken
State-of-the-art NLP methods achieve human-like performance on many tasks,
but make errors nevertheless. Characterizing these errors in easily
interpretable terms gives insight into whether a classifier is prone to making
systematic errors, but also gives a way to act and improve the classifier. We
propose to discover those patterns of tokens that distinguish correct and
erroneous predictions as to obtain global and interpretable descriptions for
arbitrary NLP classifiers. We formulate the problem of finding a succinct and
non-redundant set of such patterns in terms of the Minimum Description Length
principle. Through an extensive set of experiments, we show that our method,
Premise, performs well in practice. Unlike existing solutions, it recovers
ground truth, even on highly imbalanced data over large vocabularies. In VQA
and NER case studies, we confirm that it gives clear and actionable insight
into the systematic errors made by NLP classifiers.