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Hierarchical Text Generation and Planning for Strategic Dialogue

arXiv 1712.05846 ↗First posted 15 Dec 2017cs.CLcited by 23

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Abstract (arXiv)

End-to-end models for goal-orientated dialogue are challenging to train, because linguistic and strategic aspects are entangled in latent state vectors. We introduce an approach to learning representations of messages in dialogues by maximizing the likelihood of subsequent sentences and actions, which decouples the semantics of the dialogue utterance from its linguistic realization. We then use these latent sentence representations for hierarchical language generation, planning and reinforcement learning. Experiments show that our approach increases the end-task reward achieved by the model, improves the effectiveness of long-term planning using rollouts, and allows self-play reinforcement learning to improve decision making without diverging from human language. Our hierarchical latent-variable model outperforms previous work both linguistically and strategically.

Key facts. Cited by 23 papers in the map (data through 14 Oct 2026). First posted on arXiv 15 Dec 2017.

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Citing papers per year: 2021: 7; 2022: 6; 2023: 4; 2024: 2; 2025: 2; 2026: 2.

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  1. 2023The Curious Decline of Linguistic Diversity: Training Language Models on Synthetic Textcited by 100
  2. 2021Modeling Strong and Human-Like Gameplay with KL-Regularized Searchcited by 50
  3. 2024Benchmarking Linguistic Diversity of Large Language Modelscited by 38
  4. 2022CHAI: A CHatbot AI for Task-Oriented Dialogue with Offline Reinforcement Learningcited by 34
  5. 2021Reason first, then respond: Modular Generation for Knowledge-infused Dialoguecited by 32
  6. 2021Compositional Transformers for Scene Generationcited by 24
  7. 2022A Mixture-of-Expert Approach to RL-based Dialogue Managementcited by 16
  8. 2022Measuring and Improving Semantic Diversity of Dialogue Generationcited by 16
  9. 2025Top-Down Semantic Refinement for Image Captioningcited by 16
  10. 2022Recent Advances in Neural Text Generation: A Task-Agnostic Surveycited by 14
  11. 2022Social Influence Dialogue Systems: A Survey of Datasets and Models For Social Influence Taskscited by 14
  12. 2023Reinforcement Learning for Generative AI: A Surveycited by 11
11 more
  1. 2021Maintaining Common Ground in Dynamic Environmentscited by 9
  2. 2021Situated Dialogue Learning through Procedural Environment Generationcited by 9
  3. 2026TERMS-Bench: Diagnosing LLM Negotiation Agents Beyond Deal Ratecited by 9
  4. 2022Towards a Progression-Aware Autonomous Dialogue Agentcited by 7
  5. 2024Target-constrained Bidirectional Planning for Generation of Target-oriented Proactive Dialoguecited by 6
  6. 2023Symbolic Planning and Code Generation for Grounded Dialoguecited by 5
  7. 2025We Argue to Agree: Towards Personality-Driven Argumentation-Based Negotiation Dialogue Systems for Tourismcited by 5
  8. 2021Targeted Data Acquisition for Evolving Negotiation Agentscited by 3
  9. 2023Towards a Neural Era in Dialogue Management for Collaboration: A Literature Surveycited by 3
  10. 2021WeaSuL: Weakly Supervised Dialogue Policy Learning: Reward Estimation for Multi-turn Dialoguecited by 2
  11. 2026Learning to Sell: Reinforcement Learning for Strategic Large Language Model Agents in Multi-Product Marketscited by 1