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Text revision refers to a family of natural language generation tasks, wherethe source and target sequences share moderate resemblance in surface form butdifferentiate in attributes, such as text formality and simplicity. Currentstate-of-the-art methods formulate these tasks as sequence-to-sequence learningproblems, which rely on large-scale parallel training corpus. In this paper, wepresent an iterative in-place editing approach for text revision, whichrequires no parallel data. In this approach, we simply fine-tune a pre-trainedTransformer with masked language modeling and attribute classification. Duringinference, the editing at each iteration is realized by two-step spanreplacement. At the first step, the distributed representation of the textoptimizes on the fly towards an attribute function. At the second step, a textspan is masked and another new one is proposed conditioned on the optimizedrepresentation. The empirical experiments on two typical and important textrevision tasks, text formalization and text simplification, show theeffectiveness of our approach. It achieves competitive and even betterperformance than state-of-the-art supervised methods on text simplification,and gains better performance than strong unsupervised methods on textformalization \footnote{Code and model are available at\url{https://github.com/jingjingli01/OREO}}.

Text Revision by On-the-Fly Representation Optimization