@inproceedings{kerscher-eger-2020-vec2sent,
    title = "{V}ec2{S}ent: Probing Sentence Embeddings with Natural Language Generation",
    author = "Kerscher, Martin  and
      Eger, Steffen",
    editor = "Scott, Donia  and
      Bel, Nuria  and
      Zong, Chengqing",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2020.coling-main.152/",
    doi = "10.18653/v1/2020.coling-main.152",
    pages = "1729--1736",
    abstract = "We introspect black-box sentence embeddings by conditionally generating from them with the objective to retrieve the underlying discrete sentence. We perceive of this as a new unsupervised probing task and show that it correlates well with downstream task performance. We also illustrate how the language generated from different encoders differs. We apply our approach to generate sentence analogies from sentence embeddings."
}