Abstract
This paper presents a simple and effective discrete optimization method for training binarized knowledge graph embedding model B-CP. Unlike the prior work using a SGD-based method and quantization of real-valued vectors, the proposed method directly optimizes binary embedding vectors by a series of bit flipping operations. On the standard knowledge graph completion tasks, the B-CP model trained with the proposed method achieved comparable performance with that trained with SGD as well as state-of-the-art real-valued models with similar embedding dimensions.- Anthology ID:
- 2020.findings-emnlp.10
- 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:
- 109–114
- Language:
- URL:
- https://aclanthology.org/2020.findings-emnlp.10
- DOI:
- 10.18653/v1/2020.findings-emnlp.10
- Cite (ACL):
- Katsuhiko Hayashi, Koki Kishimoto, and Masashi Shimbo. 2020. A Greedy Bit-flip Training Algorithm for Binarized Knowledge Graph Embeddings. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 109–114, Online. Association for Computational Linguistics.
- Cite (Informal):
- A Greedy Bit-flip Training Algorithm for Binarized Knowledge Graph Embeddings (Hayashi et al., Findings 2020)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2020.findings-emnlp.10.pdf
- Data
- FB15k-237