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Retrieval-Enhanced Adversarial Training for Neural Response Generation

arXiv 1809.04276 ↗First posted 12 Sep 2018cs.CLcited by 22

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Abstract (arXiv)

Dialogue systems are usually built on either generation-based or retrieval-based approaches, yet they do not benefit from the advantages of different models. In this paper, we propose a Retrieval-Enhanced Adversarial Training (REAT) method for neural response generation. Distinct from existing approaches, the REAT method leverages an encoder-decoder framework in terms of an adversarial training paradigm, while taking advantage of N-best response candidates from a retrieval-based system to construct the discriminator. An empirical study on a large scale public available benchmark dataset shows that the REAT method significantly outperforms the vanilla Seq2Seq model as well as the conventional adversarial training approach.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Sep 2018.

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Citing papers per year: 2021: 8; 2022: 5; 2023: 5; 2024: 2; 2025: 1; 2026: 1.

22 papers.

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  1. 2021Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Surveycited by 130
  2. 2023A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?cited by 84
  3. 2021TextStyleBrush: Transfer of Text Aesthetics from a Single Examplecited by 48
  4. 2021De-rendering Stylized Textscited by 24
  5. 2023AGTGAN: Unpaired Image Translation for Photographic Ancient Character Generationcited by 17
  6. 2021STRIVE: Scene Text Replacement In Videoscited by 13
  7. 2021Recall and Learn: A Memory-augmented Solver for Math Word Problemscited by 13
  8. 2022Report from the NSF Future Directions Workshop on Automatic Evaluation of Dialog: Research Directions and Challengescited by 13
  9. 2022GenText: Unsupervised Artistic Text Generation via Decoupled Font and Texture Manipulationcited by 6
  10. 2021A Template-guided Hybrid Pointer Network for Knowledge-basedTask-oriented Dialogue Systemscited by 5
  11. 2023Alleviating the Long-Tail Problem in Conversational Recommender Systemscited by 5
  12. 2025Communication is All You Need: Persuasion Dataset Construction via Multi-LLM Communicationcited by 5
10 more
  1. 2021Scalable Font Reconstruction with Dual Latent Manifoldscited by 4
  2. 2022A Survey on Natural Language Processing for Programmingcited by 3
  3. 2022Lexical Knowledge Internalization for Neural Dialog Generationcited by 1
  4. 2022CORAL: Contextual Response Retrievability Loss Function for Training Dialog Generation Modelscited by 1
  5. 2021A Neural Conversation Generation Model via Equivalent Shared Memory Investigationcited by 0
  6. 2023HeroNet: A Hybrid Retrieval-Generation Network for Conversational Botscited by 0
  7. 2023A Conditional Generative Chatbot using Transformer Modelcited by 0
  8. 2024A Stack-Propagation Framework for Low-Resource Personalized Dialogue Generationcited by 0
  9. 2024A Static and Dynamic Attention Framework for Multi Turn Dialogue Generationcited by 0
  10. 2026SpheriCity: Designing Trustworthy Conversational AI for Sustainability Decision Supportcited by 0