mlsanityData to 7 Oct 2026

Dual Supervised Learning for Natural Language Understanding and Generation

arXiv 1905.06196 ↗First posted 15 May 2019cs.CLcited by 14

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Abstract (arXiv)

Natural language understanding (NLU) and natural language generation (NLG) are both critical research topics in the NLP field. Natural language understanding is to extract the core semantic meaning from the given utterances, while natural language generation is opposite, of which the goal is to construct corresponding sentences based on the given semantics. However, such dual relationship has not been investigated in the literature. This paper proposes a new learning framework for language understanding and generation on top of dual supervised learning, providing a way to exploit the duality. The preliminary experiments show that the proposed approach boosts the performance for both tasks.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 May 2019.

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Citing papers per year: 2021: 4; 2022: 1; 2023: 5; 2024: 2; 2025: 1; 2026: 1.

14 papers.

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  1. 2021Want To Reduce Labeling Cost? GPT-3 Can Helpcited by 196
  2. 2021Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Surveycited by 130
  3. 2023A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?cited by 84
  4. 2021LearnDA: Learnable Knowledge-Guided Data Augmentation for Event Causality Identificationcited by 16
  5. 2021Jointly Improving Language Understanding and Generation with Quality-Weighted Weak Supervision of Automatic Labelingcited by 11
  6. 2025Overcoming Data Scarcity in Generative Language Modelling for Low-Resource Languages: A Systematic Reviewcited by 8
  7. 2023A Bi-directional Multi-hop Inference Model for Joint Dialog Sentiment Classification and Act Recognitioncited by 2
  8. 2023DualTalker: A Cross-Modal Dual Learning Approach for Speech-Driven 3D Facial Animationcited by 2
  9. 2022Leveraging Key Information Modeling to Improve Less-Data Constrained News Headline Generation via Duality Fine-Tuningcited by 1
  10. 2023Language as a Latent Sequence: deep latent variable models for semi-supervised paraphrase generationcited by 1
  11. 2023A Unified Framework for Slot based Response Generation in a Multimodal Dialogue Systemcited by 1
  12. 2024Chem-FINESE: Validating Fine-Grained Few-shot Entity Extraction through Text Reconstructioncited by 1
2 more
  1. 2024Synergistic Dual Spatial-aware Generation of Image-to-Text and Text-to-Imagecited by 1
  2. 2026The Generation-Recognition Asymmetry: Six Dimensions of a Fundamental Divide in Formal Language Theorycited by 1