@inproceedings{yang-klein-2021-fudge,
    title = "{FUDGE}: Controlled Text Generation With Future Discriminators",
    author = "Yang, Kevin  and
      Klein, Dan",
    editor = "Toutanova, Kristina  and
      Rumshisky, Anna  and
      Zettlemoyer, Luke  and
      Hakkani-Tur, Dilek  and
      Beltagy, Iz  and
      Bethard, Steven  and
      Cotterell, Ryan  and
      Chakraborty, Tanmoy  and
      Zhou, Yichao",
    booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.naacl-main.276/",
    doi = "10.18653/v1/2021.naacl-main.276",
    pages = "3511--3535",
    abstract = "We propose Future Discriminators for Generation (FUDGE), a flexible and modular method for controlled text generation. Given a pre-existing model G for generating text from a distribution of interest, FUDGE enables conditioning on a desired attribute a (for example, formality) while requiring access only to G{'}s output logits. FUDGE learns an attribute predictor operating on a partial sequence, and uses this predictor{'}s outputs to adjust G{'}s original probabilities. We show that FUDGE models terms corresponding to a Bayesian decomposition of the conditional distribution of G given attribute a. Moreover, FUDGE can easily compose predictors for multiple desired attributes. We evaluate FUDGE on three tasks {---} couplet completion in poetry, topic control in language generation, and formality change in machine translation {---} and observe gains in all three tasks."
}