Iterative Paraphrastic Augmentation with Discriminative Span Alignment
Published
2022-01-04
Ryan Culkin
,
J. Edward Hu
,
Elias Stengel-Eskin
,
Guanghui Qin
,
Benjamin Van Durme
Ryan Culkin
Johns Hopkins University
J. Edward Hu
Johns Hopkins University
Elias Stengel-Eskin
Johns Hopkins University
Guanghui Qin
Johns Hopkins University
Benjamin Van Durme
Johns Hopkins University
Abstract
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.
Article at MIT Press
Presented at EMNLP 2021