Abstract
This paper studies the problem of injecting factual knowledge into large pre-trained language models. We train adapter modules on parts of the ConceptNet knowledge graph using the masked language modeling objective and evaluate the success of the method by a series of probing experiments on the LAMA probe. Mean P@K curves for different configurations indicate that the technique is effective, increasing the performance on sub-sets of the LAMA probe for large values of k by adding as little as 2.1% additional parameters to the original models.- Anthology ID:
- 2022.textgraphs-1.6
- Volume:
- Proceedings of TextGraphs-16: Graph-based Methods for Natural Language Processing
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Dmitry Ustalov, Yanjun Gao, Alexander Panchenko, Marco Valentino, Mokanarangan Thayaparan, Thien Huu Nguyen, Gerald Penn, Arti Ramesh, Abhik Jana
- Venue:
- TextGraphs
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 54–59
- Language:
- URL:
- https://aclanthology.org/2022.textgraphs-1.6
- DOI:
- Cite (ACL):
- Sondre Wold. 2022. The Effectiveness of Masked Language Modeling and Adapters for Factual Knowledge Injection. In Proceedings of TextGraphs-16: Graph-based Methods for Natural Language Processing, pages 54–59, Gyeongju, Republic of Korea. Association for Computational Linguistics.
- Cite (Informal):
- The Effectiveness of Masked Language Modeling and Adapters for Factual Knowledge Injection (Wold, TextGraphs 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2022.textgraphs-1.6.pdf
- Code
- sondrewold/adapters-mlm-injection
- Data
- ConceptNet, GLUE, LAMA