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Real-world text applications often involve composing a wide range of textcontrol operations, such as editing the text w.r.t. an attribute, manipulatingkeywords and structure, and generating new text of desired properties. Priorwork typically learns/finetunes a language model (LM) to perform individual orspecific subsets of operations. Recent research has studied combiningoperations in a plug-and-play manner, often with costly search or optimizationin the complex sequence space. This paper proposes a new efficient approach forcomposable text operations in the compact latent space of text. Thelow-dimensionality and differentiability of the text latent vector allow us todevelop an efficient sampler based on ordinary differential equations (ODEs)given arbitrary plug-in operators (e.g., attribute classifiers). By connectingpretrained LMs (e.g., GPT2) to the latent space through efficient adaption, wethen decode the sampled vectors into desired text sequences. The flexibleapproach permits diverse control operators (sentiment, tense, formality,keywords, etc.) acquired using any relevant data from different domains.Experiments show that composing those operators within our approach manages togenerate or edit high-quality text, substantially improving over previousmethods in terms of generation quality and efficiency.

Composable Text Controls in Latent Space with ODEs