  Many language tasks (e.g., Named Entity Recognition, Part-of-Speech tagging,
and Semantic Role Labeling) are naturally framed as sequence tagging problems.
However, there has been comparatively little work on interpretability methods
for sequence tagging models. In this paper, we extend influence functions -
which aim to trace predictions back to the training points that informed them -
to sequence tagging tasks. We define the influence of a training instance
segment as the effect that perturbing the labels within this segment has on a
test segment level prediction. We provide an efficient approximation to compute
this, and show that it tracks with the true segment influence, measured
empirically. We show the practical utility of segment influence by using the
method to identify systematic annotation errors in two named entity recognition
corpora. Code to reproduce our results is available at
https://github.com/successar/Segment_Influence_Functions.
