@InProceedings{pagliardini-gupta-jaggi:2018:N18-1,
  author    = {Pagliardini, Matteo  and  Gupta, Prakhar  and  Jaggi, Martin},
  title     = {Unsupervised Learning of Sentence Embeddings Using Compositional n-Gram Features},
  booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)},
  month     = {June},
  year      = {2018},
  address   = {New Orleans, Louisiana},
  publisher = {Association for Computational Linguistics},
  pages     = {528--540},
  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.},
  url       = {http://www.aclweb.org/anthology/N18-1049}
}