  Interpretable rationales for model predictions play a critical role in
practical applications. In this study, we develop models possessing
interpretable inference process for structured prediction. Specifically, we
present a method of instance-based learning that learns similarities between
spans. At inference time, each span is assigned a class label based on its
similar spans in the training set, where it is easy to understand how much each
training instance contributes to the predictions. Through empirical analysis on
named entity recognition, we demonstrate that our method enables to build
models that have high interpretability without sacrificing performance.
