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doi:10.22028/D291-38638
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 hdl:20.500.11880/34834 http://dx.doi.org/10.22028/D291-38638 |
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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