Iterative Paraphrastic Augmentation with Discriminative Span Alignment

Ryan Culkin, J. Edward Hu, Elias Stengel-Eskin, Guanghui Qin, Benjamin Van Durme


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.


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