Papers with long-tailed distribution issue

1 papers
Towards Realistic Low-resource Relation Extraction: A Benchmark with Empirical Baseline Study (2022.findings-emnlp)

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Challenge: Existing approaches to extract relational facts from text are limited in their ability to learn from limited labeled data.
Approach: They propose to use prompt-based methods with few-shot labeled data to evaluate performance . data augmentation technologies and self-training are also proposed to generate more labeles in-domain data.
Outcome: The proposed methods perform well in low-resource settings with 8 relation extraction datasets.

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