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arXiv · 1606.07822

Efficient Parallel Learning of Word2Vec

Jeroen B. P. Vuurens·Carsten Eickhoff·Arjen P. de Vries
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Abstract

Since its introduction, Word2Vec and its variants are widely used to learn semantics-preserving representations of words or entities in an embedding space, which can be used to produce state-of-art results for various Natural Language Processing tasks. Existing implementations aim to learn efficiently by running multiple threads in parallel while operating on a single model in shared memory, ignoring incidental memory update collisions. We show that these collisions can degrade the efficiency of parallel learning, and propose a straightforward caching strategy that improves the efficiency by a factor of 4.

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Jeroen B. P. Vuurens, Carsten Eickhoff, Arjen P. de Vries. 2016-06-24. Efficient Parallel Learning of Word2Vec. https://arxiv.org/abs/1606.07822

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