mlsanityData to 14 Oct 2026

Towards Attribute-Entangled Controllable Text Generation: A Pilot Study of Blessing Generation

arXiv 2210.16557 ↗First posted 29 Oct 2022cs.CLcited by 4

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Abstract (arXiv)

Controllable Text Generation (CTG) has obtained great success due to its fine-grained generation ability obtained by focusing on multiple attributes. However, most existing CTG researches overlook how to utilize the attribute entanglement to enhance the diversity of the controlled generated texts. Facing this dilemma, we focus on a novel CTG scenario, i.e., blessing generation which is challenging because high-quality blessing texts require CTG models to comprehensively consider the entanglement between multiple attributes (e.g., objects and occasions). To promote the research on blessing generation, we present EBleT, a large-scale Entangled Blessing Text dataset containing 293K English sentences annotated with multiple attributes. Furthermore, we propose novel evaluation metrics to measure the quality of the blessing texts generated by the baseline models we designed. Our study opens a new research direction for controllable text generation and enables the development of attribute-entangled CTG models. Our dataset and source codes are available at urlhttps://github.com/huangshulin123/Blessing-Generation.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 20 of its 45 references that are in the map. First posted on arXiv 29 Oct 2022.

Builds on

20 of 45 references.

References · 20

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  10. 2018Dear Sir or Madam, May I introduce the GYAFC Dataset: Corpus, Benchmarks and Metrics for Formality Style Transfercited by 211
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  12. 2020MEGATRON-CNTRL: Controllable Story Generation with External Knowledge Using Large-Scale Language Modelscited by 74
8 more
  1. 2021Are we ready for a new paradigm shift? A Survey on Visual Deep MLPcited by 63
  2. 2020Exploring Controllable Text Generation Techniquescited by 55
  3. 2017Affect-LM: A Neural Language Model for Customizable Affective Text Generationcited by 50
  4. 2021Transformer-based Conditional Variational Autoencoder for Controllable Story Generationcited by 44
  5. 2019Pre-train and Plug-in: Flexible Conditional Text Generation with Variational Auto-Encoderscited by 27
  6. 2020MixPoet: Diverse Poetry Generation via Learning Controllable Mixed Latent Spacecited by 17
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Cited by year

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

4 papers.

  1. 2023LatEval: An Interactive LLMs Evaluation Benchmark with Incomplete Information from Lateral Thinking Puzzlescited by 41
  2. 2024When LLMs Meet Cunning Texts: A Fallacy Understanding Benchmark for Large Language Modelscited by 22
  3. 2022Active Relation Discovery: Towards General and Label-aware Open Relation Extractioncited by 10
  4. 2023From Retrieval to Generation: Efficient and Effective Entity Set Expansioncited by 3