Please use this identifier to cite or link to this item: doi:10.22028/D291-38638
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Title: The SelectGen Challenge: Finding the Best Training Samples for Few-Shot Neural Text Generation
Author(s): Chang, Ernie
Shen, Xiaoyu
Alex, Marin
Demberg, Vera
Editor(s): Belz, Anya
Fan, Angela
Reiter, Ehud
Sripada, Yaji
Language: English
Title: Proceedings of the 14th International Conference on Natural Language Generation
Pages: 325-330
Publisher/Platform: ACL
Year of Publication: 2021
Place of the conference: Aberdeen, Scotland, United Kingdom
DDC notations: 400 Language, linguistics
Publikation type: Conference Paper
Abstract: We propose a shared task on training instance selection for few-shot neural text generation. Large-scale pretrained language models have led to dramatic improvements in few-shot text generation. Nonetheless, almost all previous work simply applies random sampling to select the few-shot training instances. Little to no attention has been paid to the selection strategies and how they would affect model performance. Studying the selection strategy can help us (1) make the most use of our annotation budget in downstream tasks and (2) better benchmark few-shot text generative models. We welcome submissions that present their selection strategies and the effects on the generation quality.
Link to this record: urn:nbn:de:bsz:291--ds-386381
Date of registration: 2-Jan-2023
Faculty: MI - Fakultät für Mathematik und Informatik
Department: MI - Informatik
Professorship: MI - Prof. Dr. Vera Demberg
Collections:SciDok - Der Wissenschaftsserver der Universität des Saarlandes

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