News

Unsupervised Learning of Sentence Embeddings

Key points
Scientific paper
AUTHOR:
Matteo Pagliardini, Prakhar Gupta, Martin Jaggi
DATE POSTED:
19/09/17

Unsupervised Learning of Sentence Embeddings

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.

by Matteo Pagliardini, Prakhar Gupta, Martin Jaggi

Codes and Models are available

https://github.com/epfml/sent2vec

https://arxiv.org/abs/1703.02507