  We investigate how disagreement in natural language inference (NLI)
annotation arises. We developed a taxonomy of disagreement sources with 10
categories spanning 3 high-level classes. We found that some disagreements are
due to uncertainty in the sentence meaning, others to annotator biases and task
artifacts, leading to different interpretations of the label distribution. We
explore two modeling approaches for detecting items with potential
disagreement: a 4-way classification with a "Complicated" label in addition to
the three standard NLI labels, and a multilabel classification approach. We
found that the multilabel classification is more expressive and gives better
recall of the possible interpretations in the data.
