# A Natural Language Corpus of Common Grounding under Continuous and Partially-Observable Context

arXiv: https://arxiv.org/abs/1907.03399 · HTML: https://mlsanity.com/paper/1907.03399/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2019.

## Built on by (most cited first, 30 of 35)

- [MindCraft: Theory of Mind Modeling for Situated Dialogue in Collaborative Tasks](https://mlsanity.com/paper/2109.06275/) (2021)
- [Learning Rewards from Linguistic Feedback](https://mlsanity.com/paper/2009.14715/) (2020)
- [Learning to Ground Multi-Agent Communication with Autoencoders](https://mlsanity.com/paper/2110.15349/) (2021)
- [Decision-Oriented Dialogue for Human-AI Collaboration](https://mlsanity.com/paper/2305.20076/) (2023)
- [Continual Learning for Instruction Following from Realtime Feedback](https://mlsanity.com/paper/2212.09710/) (2022)
- [Effect of Visual Extensions on Natural Language Understanding in Vision-and-Language Models](https://mlsanity.com/paper/2104.08066/) (2021)
- [Reference-Centric Models for Grounded Collaborative Dialogue](https://mlsanity.com/paper/2109.05042/) (2021)
- [Efficacy of Language Model Self-Play in Non-Zero-Sum Games](https://mlsanity.com/paper/2406.18872/) (2024)
- [Draw Me a Flower: Processing and Grounding Abstraction in Natural Language](https://mlsanity.com/paper/2106.14321/) (2021)
- [Pragmatics in Language Grounding: Phenomena, Tasks, and Modeling Approaches](https://mlsanity.com/paper/2211.08371/) (2022)
- [Grounding Language in Multi-Perspective Referential Communication](https://mlsanity.com/paper/2410.03959/) (2024)
- [Maintaining Common Ground in Dynamic Environments](https://mlsanity.com/paper/2105.14207/) (2021)
- [Analysis of Language Change in Collaborative Instruction Following](https://mlsanity.com/paper/2109.04452/) (2021)
- [Dealing with Semantic Underspecification in Multimodal NLP](https://mlsanity.com/paper/2306.05240/) (2023)
- [Conversational Grounding: Annotation and Analysis of Grounding Acts and Grounding Units](https://mlsanity.com/paper/2403.16609/) (2024)
- [What A Situated Language-Using Agent Must be Able to Do: A Top-Down Analysis](https://mlsanity.com/paper/2302.08590/) (2023)
- [Define, Evaluate, and Improve Task-Oriented Cognitive Capabilities for Instruction Generation Models](https://mlsanity.com/paper/2301.05149/) (2022)
- [Asking More Informative Questions for Grounded Retrieval](https://mlsanity.com/paper/2311.08584/) (2023)
- [Resolving References in Visually-Grounded Dialogue via Text Generation](https://mlsanity.com/paper/2309.13430/) (2023)
- [Symbolic Planning and Code Generation for Grounded Dialogue](https://mlsanity.com/paper/2310.17140/) (2023)
- [SpaRC and SpaRP: Spatial Reasoning Characterization and Path Generation for Understanding Spatial Reasoning Capability of Large Language Models](https://mlsanity.com/paper/2406.04566/) (2024)
- ["We Demand Justice!": Towards Social Context Grounding of Political Texts](https://mlsanity.com/paper/2311.09106/) (2023)
- [Characterizing Language Use in a Collaborative Situated Game](https://mlsanity.com/paper/2512.03381/) (2025)
- [The Interplay of Task Success and Dialogue Quality: An in-depth Evaluation in Task-Oriented Visual Dialogues](https://mlsanity.com/paper/2103.11151/) (2021)
- [PoliCon: Evaluating LLMs on Achieving Diverse Political Consensus Objectives](https://mlsanity.com/paper/2505.19558/) (2025)
- [Grounded Misunderstandings in Asymmetric Dialogue: A Perspectivist Annotation Scheme for MapTask](https://mlsanity.com/paper/2511.03718/) (2025)
- [The Collaboration Tax: How Much LLM Multi-Agent Systems Pay to Coordinate](https://mlsanity.com/paper/2608.22152/) (2026)
- [Collecting Visually-Grounded Dialogue with A Game Of Sorts](https://mlsanity.com/paper/2309.05162/) (2023)
- [Distributed Partial Information Puzzles: Examining Common Ground Construction Under Epistemic Asymmetry](https://mlsanity.com/paper/2603.05450/) (2026)
- [Seeing Is Not Sharing: Some Vision-Language Models Overestimate Common Ground in Asymmetric Dialogue](https://mlsanity.com/paper/2606.31719/) (2026)

## Abstract (arXiv)

Common grounding is the process of creating, repairing and updating mutual understandings, which is a critical aspect of sophisticated human communication. However, traditional dialogue systems have limited capability of establishing common ground, and we also lack task formulations which introduce natural difficulty in terms of common grounding while enabling easy evaluation and analysis of complex models. In this paper, we propose a minimal dialogue task which requires advanced skills of common grounding under continuous and partially-observable context. Based on this task formulation, we collected a largescale dataset of 6,760 dialogues which fulfills essential requirements of natural language corpora. Our analysis of the dataset revealed important phenomena related to common grounding that need to be considered. Finally, we evaluate and analyze baseline neural models on a simple subtask that requires recognition of the created common ground. We show that simple baseline models perform decently but leave room for further improvement. Overall, we show that our proposed task will be a fundamental testbed where we can train, evaluate, and analyze dialogue system's ability for sophisticated common grounding.