  We introduce a novel paraphrastic augmentation strategy based on
sentence-level lexically constrained paraphrasing and discriminative span
alignment. Our approach allows for the large-scale expansion of existing
resources, or the rapid creation of new resources from a small,
manually-produced seed corpus. We illustrate our framework on the Berkeley
FrameNet Project, a large-scale language understanding effort spanning more
than two decades of human labor. Based on roughly four days of collecting
training data for the alignment model and approximately one day of parallel
compute, we automatically generate 495,300 unique (Frame, Trigger) combinations
annotated in context, a roughly 50x expansion atop FrameNet v1.7.
