Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features

Matteo Pagliardini; Prakhar Gupta; Martin Jaggi

doi:10.18653/v1/n18-1049

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arXiv · 1703.02507

Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features

Matteo Pagliardini·Prakhar Gupta·Martin Jaggi

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Abstract

The recent tremendous success of unsupervised word embeddings in a multitude of applications raises the obvious question if similar methods could be derived to improve embeddings (i.e. semantic representations) of word sequences as well. We present a simple but efficient unsupervised objective to train distributed representations of sentences. Our method outperforms the state-of-the-art unsupervised models on most benchmark tasks, highlighting the robustness of the produced general-purpose sentence embeddings.

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Matteo Pagliardini, Prakhar Gupta, Martin Jaggi. 2018-12-28. Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features. https://doi.org/10.18653/v1%2Fn18-1049

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