A Greedy Bit-flip Training Algorithm for Binarized Knowledge Graph Embeddings
A Greedy Bit-flip Training Algorithm for Binarized Knowledge Graph Embeddings
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DOI：
10.18653/v1/2020.findings-emnlp.10
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发表时间：
2020-11
期刊：
影响因子：
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通讯作者：
Katsuhiko Hayashi;Koki Kishimoto;M. Shimbo
中科院分区：
文献类型：
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作者：
Katsuhiko Hayashi;Koki Kishimoto;M. Shimbo
提出了一种简单有效的离散优化方法来训练二值化知识图嵌入模型B-CP。与使用基于SGD的方法和实值向量量化的先前工作不同，所提出的方法通过一系列位翻转操作直接优化二进制嵌入向量。在标准的知识图完成任务中，使用所提出的方法训练的B-CP模型与使用SGD训练的模型以及具有相似嵌入维度的最先进的实值模型具有相当的性能。
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.