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arXiv · 1907.06385

GLOSS: Generative Latent Optimization of Sentence Representations

Sidak Pal Singh·Angela Fan·Michael Auli
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Abstract

We propose a method to learn unsupervised sentence representations in a non-compositional manner based on Generative Latent Optimization. Our approach does not impose any assumptions on how words are to be combined into a sentence representation. We discuss a simple Bag of Words model as well as a variant that models word positions. Both are trained to reconstruct the sentence based on a latent code and our model can be used to generate text. Experiments show large improvements over the related Paragraph Vectors. Compared to uSIF, we achieve a relative improvement of 5% when trained on the same data and our method performs competitively to Sent2vec while trained on 30 times less data.

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Sidak Pal Singh, Angela Fan, Michael Auli. 2019-07-15. GLOSS: Generative Latent Optimization of Sentence Representations. https://arxiv.org/abs/1907.06385

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