mlsanityData to 14 Oct 2026

PCAE: A Framework of Plug-in Conditional Auto-Encoder for Controllable Text Generation

arXiv 2210.03496 ↗First posted 7 Oct 2022cs.CLcited by 4

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Abstract (arXiv)

Controllable text generation has taken a gigantic step forward these days. Yet existing methods are either constrained in a one-off pattern or not efficient enough for receiving multiple conditions at every generation stage. We propose a model-agnostic framework Plug-in Conditional Auto-Encoder for Controllable Text Generation (PCAE) towards flexible and semi-supervised text generation. Our framework is "plug-and-play" with partial parameters to be fine-tuned in the pre-trained model (less than a half). Crucial to the success of PCAE is the proposed broadcasting label fusion network for navigating the global latent code to a specified local and confined space. Visualization of the local latent prior well confirms the primary devotion in hidden space of the proposed model. Moreover, extensive experiments across five related generation tasks (from 2 conditions up to 10 conditions) on both RNN- based and pre-trained BART [26] based auto-encoders reveal the high capability of PCAE, which enables generation that is highly manipulable, syntactically diverse and time-saving with minimum labeled samples. We will release our code at https://github.com/ImKeTT/pcae.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 41 of its 59 references that are in the map. First posted on arXiv 7 Oct 2022.

Builds on

41 of 59 references.

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Cited by year

Citing papers per year: 2022: 1; 2023: 2; 2024: 1.

4 papers.

  1. 2023ZeroGen: Zero-shot Multimodal Controllable Text Generation with Multiple Oraclescited by 5
  2. 2022An Overview on Controllable Text Generation via Variational Auto-Encoderscited by 1
  3. 2023Language Model as an Annotator: Unsupervised Context-aware Quality Phrase Generationcited by 1
  4. 2024Geo-FuB: A Method for Constructing an Operator-Function Knowledge Base for Geospatial Code Generation Tasks Using Large Language Modelscited by 1