%0 Journal Article %T Iterative Paraphrastic Augmentation with Discriminative Span Alignment %A Culkin, Ryan %A Hu, J. Edward %A Stengel-Eskin, Elias %A Qin, Guanghui %A Durme, Benjamin Van %J Transactions of the Association for Computational Linguistics %D 2021 %V 9 %I MIT Press %C Cambridge, MA %F culkin-etal-2021-iterative %X 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 datasets or the rapid creation of new datasets using a small, manually produced seed corpus. We demonstrate our approach with experiments on the Berkeley FrameNet Project, a large-scale language understanding effort spanning more than two decades of human labor. With four days of training data collection for a span alignment model and one day of parallel compute, we automatically generate and release to the community 495,300 unique (Frame,Trigger) pairs in diverse sentential contexts, a roughly 50-fold expansion atop FrameNet v1.7. The resulting dataset is intrinsically and extrinsically evaluated in detail, showing positive results on a downstream task. %R 10.1162/tacl_a_00380 %U https://aclanthology.org/2021.tacl-1.30 %U https://doi.org/10.1162/tacl_a_00380 %P 494-509