The SelectGen Challenge: Finding the Best Training Samples for Few-Shot Neural Text Generation

• URL: http://arxiv.org/abs/2108.06614v1
• Date: Sat, 14 Aug 2021 21:20:35 GMT
• Title: The SelectGen Challenge: Finding the Best Training Samples for Few-Shot Neural Text Generation
• Authors: Ernie Chang, Xiaoyu Shen, Alex Marin, Vera Demberg
• Abstract summary: We propose a shared task on training instance selection for few-shot neural text generation. The study of the selection strategy can help us to (1) make the most use of our annotation budget in downstream tasks and (2) better benchmark few-shot text generative models.
• Score: 11.534198637625208
• License: http://creativecommons.org/licenses/by/4.0/
• 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. The study of the selection strategy can help us to (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.

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