Revisiting Few-shot Relation Classification: Evaluation Data and Classification Schemes

@article{Sabo2021RevisitingFR,
  title={Revisiting Few-shot Relation Classification: Evaluation Data and Classification Schemes},
  author={O. Sabo and Yanai Elazar and Yoav Goldberg and Ido Dagan},
  journal={Transactions of the Association for Computational Linguistics},
  year={2021},
  volume={9},
  pages={691-706}
}
  • O. Sabo, Yanai Elazar, Ido Dagan
  • Published 17 April 2021
  • Computer Science
  • Transactions of the Association for Computational Linguistics
We explore few-shot learning (FSL) for relation classification (RC). Focusing on the realistic scenario of FSL, in which a test instance might not belong to any of the target categories (none-of-the-above, [NOTA]), we first revisit the recent popular dataset structure for FSL, pointing out its unrealistic data distribution. To remedy this, we propose a novel methodology for deriving more realistic few-shot test data from available datasets for supervised RC, and apply it to the TACRED dataset… 

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