E-BERT: Efficient-Yet-Effective Entity Embeddings for BERT

Nina Poerner, Ulli Waltinger, Hinrich Schütze


Abstract
We present a novel way of injecting factual knowledge about entities into the pretrained BERT model (Devlin et al., 2019): We align Wikipedia2Vec entity vectors (Yamada et al., 2016) with BERT’s native wordpiece vector space and use the aligned entity vectors as if they were wordpiece vectors. The resulting entity-enhanced version of BERT (called E-BERT) is similar in spirit to ERNIE (Zhang et al., 2019) and KnowBert (Peters et al., 2019), but it requires no expensive further pre-training of the BERT encoder. We evaluate E-BERT on unsupervised question answering (QA), supervised relation classification (RC) and entity linking (EL). On all three tasks, E-BERT outperforms BERT and other baselines. We also show quantitatively that the original BERT model is overly reliant on the surface form of entity names (e.g., guessing that someone with an Italian-sounding name speaks Italian), and that E-BERT mitigates this problem.
Anthology ID:
2020.findings-emnlp.71
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2020
Month:
November
Year:
2020
Address:
Online
Editors:
Trevor Cohn, Yulan He, Yang Liu
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
803–818
Language:
URL:
https://aclanthology.org/2020.findings-emnlp.71
DOI:
10.18653/v1/2020.findings-emnlp.71
Bibkey:
Cite (ACL):
Nina Poerner, Ulli Waltinger, and Hinrich Schütze. 2020. E-BERT: Efficient-Yet-Effective Entity Embeddings for BERT. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 803–818, Online. Association for Computational Linguistics.
Cite (Informal):
E-BERT: Efficient-Yet-Effective Entity Embeddings for BERT (Poerner et al., Findings 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.findings-emnlp.71.pdf
Optional supplementary material:
 2020.findings-emnlp.71.OptionalSupplementaryMaterial.pdf
Video:
 https://slideslive.com/38940166
Data
LAMAT-REx