# Characterizing Language Use in a Collaborative Situated Game

arXiv: https://arxiv.org/abs/2512.03381 · HTML: https://mlsanity.com/paper/2512.03381/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 88 references that are in the map.
- First posted on arXiv 3 Dec 2025.

## Builds on (most cited first, 27 of 27)

- [MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling](https://mlsanity.com/paper/1810.00278/) (2018)
- [On the Utility of Learning about Humans for Human-AI Coordination](https://mlsanity.com/paper/1910.05789/) (2019)
- [WhisperX: Time-Accurate Speech Transcription of Long-Form Audio](https://mlsanity.com/paper/2303.00747/) (2023)
- [Deal or No Deal? End-to-End Learning for Negotiation Dialogues](https://mlsanity.com/paper/1706.05125/) (2017)
- [The Hanabi Challenge: A New Frontier for AI Research](https://mlsanity.com/paper/1902.00506/) (2019)
- [Decoupling Strategy and Generation in Negotiation Dialogues](https://mlsanity.com/paper/1808.09637/) (2018)
- [Collaborating with Humans without Human Data](https://mlsanity.com/paper/2110.08176/) (2021)
- [Task-Oriented Dialogue as Dataflow Synthesis](https://mlsanity.com/paper/2009.11423/) (2020)
- [Decomposing Uncertainty for Large Language Models through Input Clarification Ensembling](https://mlsanity.com/paper/2311.08718/) (2023)
- [Clarify When Necessary: Resolving Ambiguity Through Interaction with LMs](https://mlsanity.com/paper/2311.09469/) (2023)
- [Executing Instructions in Situated Collaborative Interactions](https://mlsanity.com/paper/1910.03655/) (2019)
- [MindCraft: Theory of Mind Modeling for Situated Dialogue in Collaborative Tasks](https://mlsanity.com/paper/2109.06275/) (2021)
- [Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings](https://mlsanity.com/paper/1704.07130/) (2017)
- [LILO: Learning Interpretable Libraries by Compressing and Documenting Code](https://mlsanity.com/paper/2310.19791/) (2023)
- [Grounding Gaps in Language Model Generations](https://mlsanity.com/paper/2311.09144/) (2023)
- [A Natural Language Corpus of Common Grounding under Continuous and Partially-Observable Context](https://mlsanity.com/paper/1907.03399/) (2019)
- [Characterizing the dynamics of learning in repeated reference games](https://mlsanity.com/paper/1912.07199/) (2019)
- [A Survey of Available Corpora for Building Data-Driven Dialogue Systems](https://mlsanity.com/paper/1512.05742/) (2015)
- [Asking the Right Question at the Right Time: Human and Model Uncertainty Guidance to Ask Clarification Questions](https://mlsanity.com/paper/2402.06509/) (2024)
- [Talk Less, Interact Better: Evaluating In-context Conversational Adaptation in Multimodal LLMs](https://mlsanity.com/paper/2408.01417/) (2024)
- [Learning to communicate about shared procedural abstractions](https://mlsanity.com/paper/2107.00077/) (2021)
- ["You Are An Expert Linguistic Annotator": Limits of LLMs as Analyzers of Abstract Meaning Representation](https://mlsanity.com/paper/2310.17793/) (2023)
- [Grounding Language in Multi-Perspective Referential Communication](https://mlsanity.com/paper/2410.03959/) (2024)
- [The timing bottleneck: Why timing and overlap are mission-critical for conversational user interfaces, speech recognition and dialogue systems](https://mlsanity.com/paper/2307.15493/) (2023)
- [Conversational Grounding: Annotation and Analysis of Grounding Acts and Grounding Units](https://mlsanity.com/paper/2403.16609/) (2024)
- [A Dialogue Game for Eliciting Balanced Collaboration](https://mlsanity.com/paper/2406.08202/) (2024)
- [Collaborative Problem-Solving in an Optimization Game](https://mlsanity.com/paper/2505.15490/) (2025)

## Built on by (most cited first, 3 of 3)

- [One Frozen Simulator Is Not Enough: Simulator Collapse in Multi-Agent RL](https://mlsanity.com/paper/2608.12253/) (2026)
- [Effective Explanations Support Planning Under Uncertainty](https://mlsanity.com/paper/2605.08406/) (2026)
- [DigitalCoach: Communication and Grounding Gaps in Human and Agentic Computer Use Coaching](https://mlsanity.com/paper/2606.31980/) (2026)

## Abstract (arXiv)

Cooperative video games, where multiple participants must coordinate by communicating and reasoning under uncertainty in complex environments, yield a rich source of language data. We collect the Portal Dialogue Corpus: a corpus of 11.5 hours of spoken human dialogue in the co-op mode of the popular Portal 2 virtual puzzle game, comprising 24.5K total utterances. We analyze player language and behavior, identifying a number of linguistic phenomena that rarely appear in most existing chitchat or task-oriented dialogue corpora, including complex spatial reference, clarification and repair, and ad-hoc convention formation. To support future analyses of language use in complex, situated, collaborative problem-solving scenarios, we publicly release the corpus, which comprises player videos, audio, transcripts, game state data, and both manual and automatic annotations of language data.