Iterative Paraphrastic Augmentation with Discriminative Span Alignment
Ryan Culkin, J. Edward Hu, Elias Stengel-Eskin, Guanghui Qin, Benjamin Van Durme
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.- Anthology ID:
- 2021.tacl-1.30
- Volume:
- Transactions of the Association for Computational Linguistics, Volume 9
- Month:
- Year:
- 2021
- Address:
- Cambridge, MA
- Editors:
- Brian Roark, Ani Nenkova
- Venue:
- TACL
- SIG:
- Publisher:
- MIT Press
- Note:
- Pages:
- 494–509
- Language:
- URL:
- https://aclanthology.org/2021.tacl-1.30
- DOI:
- 10.1162/tacl_a_00380
- Cite (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.
- Cite (Informal):
- Iterative Paraphrastic Augmentation with Discriminative Span Alignment (Culkin et al., TACL 2021)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2021.tacl-1.30.pdf