@inproceedings{hayashi-etal-2020-greedy,
    title = "A Greedy Bit-flip Training Algorithm for Binarized Knowledge Graph Embeddings",
    author = "Hayashi, Katsuhiko  and
      Kishimoto, Koki  and
      Shimbo, Masashi",
    editor = "Cohn, Trevor  and
      He, Yulan  and
      Liu, Yang",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2020.findings-emnlp.10/",
    doi = "10.18653/v1/2020.findings-emnlp.10",
    pages = "109--114",
    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."
}