A Greedy Bit-flip Training Algorithm for Binarized Knowledge Graph Embeddings A Greedy Bit-flip Training Algorithm for Binarized Knowledge Graph Embeddings

Katsuhiko Hayashi, Koki Kishimoto, Masashi Shimbo. A Greedy Bit-flip Training Algorithm for Binarized Knowledge Graph Embeddings. Findings of the Association for Computational Linguistics: EMNLP 2020. pp.109-114. 2020. DOI: 10.18653/v1/2020.findings-emnlp.10

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.