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        <title>Iterative Paraphrastic Augmentation with Discriminative Span Alignment</title>
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    <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.</abstract>
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%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

Markdown (Informal)

[Iterative Paraphrastic Augmentation with Discriminative Span Alignment](https://aclanthology.org/2021.tacl-1.30/) (Culkin et al., TACL 2021)

• Iterative Paraphrastic Augmentation with Discriminative Span Alignment (Culkin et al., TACL 2021)

ACL

• Ryan Culkin, J. Edward Hu, Elias Stengel-Eskin, Guanghui Qin, and Benjamin Van Durme. 2021. Iterative Paraphrastic Augmentation with Discriminative Span Alignment. Transactions of the Association for Computational Linguistics, 9:494–509.