Language Models as Knowledge Bases: On Entity Representations, Storage Capacity, and Paraphrased Queries

Benjamin Heinzerling, Kentaro Inui


Abstract
Pretrained language models have been suggested as a possible alternative or complement to structured knowledge bases. However, this emerging LM-as-KB paradigm has so far only been considered in a very limited setting, which only allows handling 21k entities whose name is found in common LM vocabularies. Furthermore, a major benefit of this paradigm, i.e., querying the KB using natural language paraphrases, is underexplored. Here we formulate two basic requirements for treating LMs as KBs: (i) the ability to store a large number facts involving a large number of entities and (ii) the ability to query stored facts. We explore three entity representations that allow LMs to handle millions of entities and present a detailed case study on paraphrased querying of facts stored in LMs, thereby providing a proof-of-concept that language models can indeed serve as knowledge bases.
Anthology ID:
2021.eacl-main.153
Volume:
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
Month:
April
Year:
2021
Address:
Online
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1772–1791
Language:
URL:
https://aclanthology.org/2021.eacl-main.153
DOI:
10.18653/v1/2021.eacl-main.153
Bibkey:
Cite (ACL):
Benjamin Heinzerling and Kentaro Inui. 2021. Language Models as Knowledge Bases: On Entity Representations, Storage Capacity, and Paraphrased Queries. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 1772–1791, Online. Association for Computational Linguistics.
Cite (Informal):
Language Models as Knowledge Bases: On Entity Representations, Storage Capacity, and Paraphrased Queries (Heinzerling & Inui, EACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.eacl-main.153.pdf
Code
 bheinzerling/lm-as-kb