Jack Urbanek, Angela Fan, Siddharth Karamcheti, Saachi Jain, Samuel Humeau, Emily Dinan, Tim Rocktäschel, Douwe Kiela, Arthur Szlam, Jason Weston
| Challenge: | Existing studies on grounded dialogue use only statistical regularities of text data, without explicit understanding of the world that the text describes. |
| Approach: | They propose a large-scale crowdsourced text adventure game as a research platform for studying grounded dialogue. |
| Outcome: | The proposed game allows agents to perceive, emote, and act whilst conducting dialogue with other agents. |
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| Challenge: | a key hypothesis in the pursuit towards creating goal-driven natural language-based agents is interactivity and environment grounding is critical for effective language learning. |
| Approach: | They augment LIGHT by learning to procedurally generate additional novel textual worlds and quests to create a curriculum of steadily increasing difficulty for training agents. |
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Deep Dungeons and Dragons: Learning Character-Action Interactions from Role-Playing Game Transcripts (N18-2)
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| Challenge: | a novel approach to understanding narratives involves modelling the interaction between characters and actions . we propose role-playing games as a testbed for inferring interactions between characters in narratives . |
| Approach: | They propose role-playing games as a testbed for learning latent ties between characters and actions . they propose to combine character and action descriptions from online discussion forums . |
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How to Motivate Your Dragon: Teaching Goal-Driven Agents to Speak and Act in Fantasy Worlds (2021.naacl-main)
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| Challenge: | a recent improvement in the quality of natural language processing and generation (NLG) is needed for goal-oriented ML driven agents. |
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Dialogue in the Wild: Learning from a Deployed Role-Playing Game with Humans and Bots (2021.findings-acl)
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| Challenge: | prevailing paradigm in natural language processing research is to build a fixed dataset and freeze it, without any ability for the model to interact with humans using language at training time at all. |
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Grounding Language in Multi-Perspective Referential Communication (2024.emnlp-main)
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| Challenge: | Using a dataset of 2,970 human-written referring expressions, we find that the performance of automated models in both reference generation and comprehension lags behind that of pairs of human agents. |
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From Word to World: Can Large Language Models be Implicit Text-based World Models? (2026.acl-long)
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Yixia Li, Hongru Wang, Jiahao Qiu, Zhenfei Yin, Dongdong Zhang, Cheng Qian, Zeping Li, Xiaoteng Ma, Guanhua Chen, Heng Ji
| Challenge: | Agentic learning increasingly hinges on interaction, yet real-world experience is expensive, limited, and often irreversible at inference time. |
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Keep CALM and Explore: Language Models for Action Generation in Text-based Games (2020.emnlp-main)
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| Challenge: | Text-based games present a unique challenge for autonomous agents to operate in natural language and handle enormous action spaces. |
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Abstract then Play: A Skill-centric Reinforcement Learning Framework for Text-based Games (2023.findings-acl)
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| Challenge: | Existing reinforcement learning frameworks fail to decompose the task and abstract the action autonomously. |
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LinguaGame: A Linguistically Grounded Game-Theoretic Paradigm for Multi-Agent Dialogue Generation (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) have enabled Multi-Agent Systems (MASs) where agents interact through natural language to solve complex tasks or simulate multi-party dialogues. |
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Reading and Acting while Blindfolded: The Need for Semantics in Text Game Agents (2021.naacl-main)
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| Challenge: | Recent work has used text-based games as a testbed for developing autonomous agents that operate using natural language. |
| Approach: | They propose an inverse dynamics decoder to regularize representation space and encourage exploration to reduce the amount of semantic information available to a learning agent. |
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