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, aka 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. This yields a new challenging benchmark for FSL RC,
on which state of the art models show poor performance. Next, we analyze classification schemes within the popular embeddingbased nearest-neighbor approach for FSL, with respect to constraints they impose on the embedding space. Triggered by this analysis we propose a novel classification scheme, in which the NOTA category is represented as learned vectors, shown empirically to be an
appealing option for FSL.

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

Virtual Poster Session III: Information Extraction

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EMNLP 2021

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible.

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

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