@inproceedings{chen-etal-2019-enteval,
title = "{E}nt{E}val: A Holistic Evaluation Benchmark for Entity Representations",
author = "Chen, Mingda and
Chu, Zewei and
Chen, Yang and
Stratos, Karl and
Gimpel, Kevin",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1040",
doi = "10.18653/v1/D19-1040",
pages = "421--433",
abstract = "Rich entity representations are useful for a wide class of problems involving entities. Despite their importance, there is no standardized benchmark that evaluates the overall quality of entity representations. In this work, we propose EntEval: a test suite of diverse tasks that require nontrivial understanding of entities including entity typing, entity similarity, entity relation prediction, and entity disambiguation. In addition, we develop training techniques for learning better entity representations by using natural hyperlink annotations in Wikipedia. We identify effective objectives for incorporating the contextual information in hyperlinks into state-of-the-art pretrained language models (Peters et al., 2018) and show that they improve strong baselines on multiple EntEval tasks.",
}
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<abstract>Rich entity representations are useful for a wide class of problems involving entities. Despite their importance, there is no standardized benchmark that evaluates the overall quality of entity representations. In this work, we propose EntEval: a test suite of diverse tasks that require nontrivial understanding of entities including entity typing, entity similarity, entity relation prediction, and entity disambiguation. In addition, we develop training techniques for learning better entity representations by using natural hyperlink annotations in Wikipedia. We identify effective objectives for incorporating the contextual information in hyperlinks into state-of-the-art pretrained language models (Peters et al., 2018) and show that they improve strong baselines on multiple EntEval tasks.</abstract>
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%0 Conference Proceedings
%T EntEval: A Holistic Evaluation Benchmark for Entity Representations
%A Chen, Mingda
%A Chu, Zewei
%A Chen, Yang
%A Stratos, Karl
%A Gimpel, Kevin
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F chen-etal-2019-enteval
%X Rich entity representations are useful for a wide class of problems involving entities. Despite their importance, there is no standardized benchmark that evaluates the overall quality of entity representations. In this work, we propose EntEval: a test suite of diverse tasks that require nontrivial understanding of entities including entity typing, entity similarity, entity relation prediction, and entity disambiguation. In addition, we develop training techniques for learning better entity representations by using natural hyperlink annotations in Wikipedia. We identify effective objectives for incorporating the contextual information in hyperlinks into state-of-the-art pretrained language models (Peters et al., 2018) and show that they improve strong baselines on multiple EntEval tasks.
%R 10.18653/v1/D19-1040
%U https://aclanthology.org/D19-1040
%U https://doi.org/10.18653/v1/D19-1040
%P 421-433
Markdown (Informal)
[EntEval: A Holistic Evaluation Benchmark for Entity Representations](https://aclanthology.org/D19-1040) (Chen et al., EMNLP-IJCNLP 2019)
ACL
- Mingda Chen, Zewei Chu, Yang Chen, Karl Stratos, and Kevin Gimpel. 2019. EntEval: A Holistic Evaluation Benchmark for Entity Representations. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 421–433, Hong Kong, China. Association for Computational Linguistics.