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We propose Future Discriminators for Generation (FUDGE), a flexible andmodular method for controlled text generation. Given a pre-existing model G forgenerating text from a distribution of interest, FUDGE enables conditioning ona desired attribute a (for example, formality) while requiring access only toG's output logits. FUDGE learns an attribute predictor operating on a partialsequence, and uses this predictor's outputs to adjust G's originalprobabilities. We show that FUDGE models terms corresponding to a Bayesiandecomposition of the conditional distribution of G given attribute a. Moreover,FUDGE can easily compose predictors for multiple desired attributes. Weevaluate FUDGE on three tasks -- couplet completion in poetry, topic control inlanguage generation, and formality change in machine translation -- and observegains in all three tasks.

FUDGE: Controlled Text Generation With Future Discriminators