  In recent years, the development of large pretrained language models, such as
BERT and GPT, significantly improved information extraction systems on various
tasks, including relation classification. State-of-the-art systems are highly
accurate on scientific benchmarks. A lack of explainability is currently a
complicating factor in many real-world applications. Comprehensible systems are
necessary to prevent biased, counterintuitive, or harmful decisions.
  We introduce semantic extents, a concept to analyze decision patterns for the
relation classification task. Semantic extents are the most influential parts
of texts concerning classification decisions. Our definition allows similar
procedures to determine semantic extents for humans and models. We provide an
annotation tool and a software framework to determine semantic extents for
humans and models conveniently and reproducibly. Comparing both reveals that
models tend to learn shortcut patterns from data. These patterns are hard to
detect with current interpretability methods, such as input reductions. Our
approach can help detect and eliminate spurious decision patterns during model
development. Semantic extents can increase the reliability and security of
natural language processing systems. Semantic extents are an essential step in
enabling applications in critical areas like healthcare or finance. Moreover,
our work opens new research directions for developing methods to explain deep
learning models.

Computation and Language;Artificial Intelligence