Abstract
Retrieve-and-edit based approaches to structured prediction, where structures associated
with retrieved neighbors are edited to form
new structures, have recently attracted increased interest. However, much recent work
merely conditions on retrieved structures (e.g.,
in a sequence-to-sequence framework), rather
than explicitly manipulating them. We show
we can perform accurate sequence labeling
by explicitly (and only) copying labels from
retrieved neighbors. Moreover, because this
copying is label-agnostic, we can achieve impressive performance in zero-shot sequencelabeling tasks. We additionally consider a dynamic programming approach to sequence labeling in the presence of retrieved neighbors,
which allows for controlling the number of
distinct (copied) segments used to form a prediction, and leads to both more interpretable
and accurate predictions.