ManyEnt: A Dataset for Few-shot Entity Typing

Markus Eberts; Kevin Pech; Adrian Ulges

COLING 2020main5 citations

ManyEnt: A Dataset for Few-shot Entity Typing

Abstract

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).

BibTeX

@inproceedings{eberts-etal-2020-manyent,
    title = "{M}any{E}nt: A Dataset for Few-shot Entity Typing",
    author = "Eberts, Markus  and
      Pech, Kevin  and
      Ulges, Adrian",
    editor = "Scott, Donia  and
      Bel, Nuria  and
      Zong, Chengqing",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://aclanthology.org/2020.coling-main.486/",
    doi = "10.18653/v1/2020.coling-main.486",
    pages = "5553--5557"
}