Embedding Compression with Isotropic Iterative Quantization

Siyu Liao; Jie Chen; Yanzhi Wang; Qinru Qiu; Bo Yuan

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arXiv · 2001.05314

Embedding Compression with Isotropic Iterative Quantization

Siyu Liao·Jie Chen·Yanzhi Wang·Qinru Qiu·Bo Yuan

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Abstract

Continuous representation of words is a standard component in deep learning-based NLP models. However, representing a large vocabulary requires significant memory, which can cause problems, particularly on resource-constrained platforms. Therefore, in this paper we propose an isotropic iterative quantization (IIQ) approach for compressing embedding vectors into binary ones, leveraging the iterative quantization technique well established for image retrieval, while satisfying the desired isotropic property of PMI based models. Experiments with pre-trained embeddings (i.e., GloVe and HDC) demonstrate a more than thirty-fold compression ratio with comparable and sometimes even improved performance over the original real-valued embedding vectors.

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BibTeX RIS

Siyu Liao, Jie Chen, Yanzhi Wang, Qinru Qiu, Bo Yuan. 2020-01-11. Embedding Compression with Isotropic Iterative Quantization. https://arxiv.org/abs/2001.05314

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