Papers › ManyEnt: A Dataset for Few-shot Entity Typing

ManyEnt: A Dataset for Few-shot Entity Typing

1 Dec 2020COLING 2020 8archive 2025-07-28

Markus Eberts, Kevin Pech, Adrian Ulges

We introduce ManyEnt, a benchmark for entity typing models in few-shot scenarios. ManyEnt offers a rich typeset, with a fine-grain variant featuring 256 entity types and a coarse-grain one with 53 entity types. Both versions have been derived from the Wikidata knowledge graph in a semi-automatic fashion. We also report results for two baselines using BERT, reaching up to 70.68{\%} accuracy (10-way 1-shot).

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Entity Typing

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Adam Attention Attention Dropout BERT Dense Connections Dropout Layer Normalization Linear Layer Linear Warmup With Linear Decay Multi-Head Attention Residual Connection Softmax Weight Decay WordPiece

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