@inproceedings{chen-etal-2019-self,
    title = "Self-Discriminative Learning for Unsupervised Document Embedding",
    author = "Chen, Hong-You  and
      Hu, Chin-Hua  and
      Wehbe, Leila  and
      Lin, Shou-De",
    editor = "Burstein, Jill  and
      Doran, Christy  and
      Solorio, Thamar",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/N19-1255/",
    doi = "10.18653/v1/N19-1255",
    pages = "2465--2474",
    abstract = "Unsupervised document representation learning is an important task providing pre-trained features for NLP applications. Unlike most previous work which learn the embedding based on self-prediction of the surface of text, we explicitly exploit the inter-document information and directly model the relations of documents in embedding space with a discriminative network and a novel objective. Extensive experiments on both small and large public datasets show the competitiveness of the proposed method. In evaluations on standard document classification, our model has errors that are 5 to 13{\%} lower than state-of-the-art unsupervised embedding models. The reduction in error is even more pronounced in scarce label setting."
}