mlsanityData to 7 Oct 2026

Learning Language Games through Interaction

arXiv 1606.02447 ↗First posted 8 Jun 2016cs.CL cs.AIcited by 43

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Abstract (arXiv)

We introduce a new language learning setting relevant to building adaptive natural language interfaces. It is inspired by Wittgenstein's language games: a human wishes to accomplish some task (e.g., achieving a certain configuration of blocks), but can only communicate with a computer, who performs the actual actions (e.g., removing all red blocks). The computer initially knows nothing about language and therefore must learn it from scratch through interaction, while the human adapts to the computer's capabilities. We created a game in a blocks world and collected interactions from 100 people playing it. First, we analyze the humans' strategies, showing that using compositionality and avoiding synonyms correlates positively with task performance. Second, we compare computer strategies, showing how to quickly learn a semantic parsing model from scratch, and that modeling pragmatics further accelerates learning for successful players.

Key facts. Cited by 43 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Jun 2016.

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Citing papers per year: 2017: 1; 2018: 0; 2019: 0; 2020: 2; 2021: 11; 2022: 13; 2023: 8; 2024: 4; 2025: 2; 2026: 2.

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  10. 2021Learning to Repair: Repairing model output errors after deployment using a dynamic memory of feedbackcited by 35
  11. 2021Few-shot Language Coordination by Modeling Theory of Mindcited by 32
  12. 2022LISA: Learning Interpretable Skill Abstractions from Languagecited by 30
31 more
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  4. 2024A Survey on Human Preference Learning for Large Language Modelscited by 23
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  6. 2021Continual Learning for Grounded Instruction Generation by Observing Human Following Behaviorcited by 19
  7. 2023Learning to Simulate Natural Language Feedback for Interactive Semantic Parsingcited by 19
  8. 2021Draw Me a Flower: Processing and Grounding Abstraction in Natural Languagecited by 14
  9. 2021FixMyPose: Pose Correctional Captioning and Retrievalcited by 12
  10. 2022Pragmatics in Language Grounding: Phenomena, Tasks, and Modeling Approachescited by 12
  11. 2023Continually Improving Extractive QA via Human Feedbackcited by 11
  12. 2025Multi-agent KTO: Reinforcing Strategic Interactions of Large Language Model in Language Gamecited by 10
  13. 2021Interscript: A dataset for interactive learning of scripts through error feedbackcited by 9
  14. 2022ASHA: Assistive Teleoperation via Human-in-the-Loop Reinforcement Learningcited by 8
  15. 2022First Contact: Unsupervised Human-Machine Co-Adaptation via Mutual Information Maximizationcited by 8
  16. 2021Improving Neural Model Performance through Natural Language Feedback on Their Explanationscited by 5
  17. 2021Designing an Automatic Agent for Repeated Language based Persuasion Gamescited by 5
  18. 2020Accelerating Reinforcement Learning Agent with EEG-based Implicit Human Feedbackcited by 4
  19. 2022X2T: Training an X-to-Text Typing Interface with Online Learning from User Feedbackcited by 4
  20. 2023"We Demand Justice!": Towards Social Context Grounding of Political Textscited by 4
  21. 2026Gesturing Toward Abstraction: Multimodal Convention Formation in Collaborative Physical Taskscited by 4
  22. 2024Representational Alignment Supports Effective Machine Teachingcited by 3
  23. 2022Memory-assisted prompt editing to improve GPT-3 after deploymentcited by 2
  24. 2022Argumentative Reward Learning: Reasoning About Human Preferencescited by 2
  25. 2022Counterfactual Explanations for Natural Language Interfacescited by 1
  26. 2022On the Limits of Evaluating Embodied Agent Model Generalization Using Validation Setscited by 1
  27. 2023Learning a Hierarchical Planner from Humans in Multiple Generationscited by 1
  28. 2024IDAT: A Multi-Modal Dataset and Toolkit for Building and Evaluating Interactive Task-Solving Agentscited by 1
  29. 2026COLLIE: Guiding Skill Discovery in Semantically Coherent Latent Spacecited by 1
  30. 2023Bootstrapping Adaptive Human-Machine Interfaces with Offline Reinforcement Learningcited by 0
  31. 2024Emergent Communication in Continuous Worlds: Self-Organisation of Conceptually Grounded Vocabularies at Scalecited by 0