@inproceedings{jeong-etal-2021-unsupervised,
    title = "Unsupervised Document Expansion for Information Retrieval with Stochastic Text Generation",
    author = "Jeong, Soyeong  and
      Baek, Jinheon  and
      Park, ChaeHun  and
      Park, Jong",
    editor = "Beltagy, Iz  and
      Cohan, Arman  and
      Feigenblat, Guy  and
      Freitag, Dayne  and
      Ghosal, Tirthankar  and
      Hall, Keith  and
      Herrmannova, Drahomira  and
      Knoth, Petr  and
      Lo, Kyle  and
      Mayr, Philipp  and
      Patton, Robert M.  and
      Shmueli-Scheuer, Michal  and
      de Waard, Anita  and
      Wang, Kuansan  and
      Wang, Lucy Lu",
    booktitle = "Proceedings of the Second Workshop on Scholarly Document Processing",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.sdp-1.2",
    doi = "10.18653/v1/2021.sdp-1.2",
    pages = "7--17",
    abstract = "One of the challenges in information retrieval (IR) is the vocabulary mismatch problem, which happens when the terms between queries and documents are lexically different but semantically similar. While recent work has proposed to expand the queries or documents by enriching their representations with additional relevant terms to address this challenge, they usually require a large volume of query-document pairs to train an expansion model. In this paper, we propose an Unsupervised Document Expansion with Generation (UDEG) framework with a pre-trained language model, which generates diverse supplementary sentences for the original document without using labels on query-document pairs for training. For generating sentences, we further stochastically perturb their embeddings to generate more diverse sentences for document expansion. We validate our framework on two standard IR benchmark datasets. The results show that our framework significantly outperforms relevant expansion baselines for IR.",
}