@inproceedings{pagliardini-etal-2018-unsupervised,
    title = "Unsupervised Learning of Sentence Embeddings Using Compositional n-Gram Features",
    author = "Pagliardini, Matteo  and
      Gupta, Prakhar  and
      Jaggi, Martin",
    editor = "Walker, Marilyn  and
      Ji, Heng  and
      Stent, Amanda",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/N18-1049/",
    doi = "10.18653/v1/N18-1049",
    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."
}