mlsanityData to 7 Oct 2026

MeetUp! A Corpus of Joint Activity Dialogues in a Visual Environment

arXiv 1907.05084 ↗First posted 11 Jul 2019cs.CL cs.CVcited by 19

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Abstract (arXiv)

Building computer systems that can converse about their visual environment is one of the oldest concerns of research in Artificial Intelligence and Computational Linguistics (see, for example, Winograd's 1972 SHRDLU system). Only recently, however, have methods from computer vision and natural language processing become powerful enough to make this vision seem more attainable. Pushed especially by developments in computer vision, many data sets and collection environments have recently been published that bring together verbal interaction and visual processing. Here, we argue that these datasets tend to oversimplify the dialogue part, and we propose a task---MeetUp!---that requires both visual and conversational grounding, and that makes stronger demands on representations of the discourse. MeetUp! is a two-player coordination game where players move in a visual environment, with the objective of finding each other. To do so, they must talk about what they see, and achieve mutual understanding. We describe a data collection and show that the resulting dialogues indeed exhibit the dialogue phenomena of interest, while also challenging the language & vision aspect.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Jul 2019.

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Citing papers per year: 2020: 1; 2021: 3; 2022: 4; 2023: 5; 2024: 2; 2025: 2; 2026: 2.

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  1. 2022Vision-and-Language Navigation: A Survey of Tasks, Methods, and Future Directionscited by 116
  2. 2020Learning Rewards from Linguistic Feedbackcited by 41
  3. 2021Effect of Visual Extensions on Natural Language Understanding in Vision-and-Language Modelscited by 18
  4. 2021Reference-Centric Models for Grounded Collaborative Dialoguecited by 15
  5. 2022Pragmatics in Language Grounding: Phenomena, Tasks, and Modeling Approachescited by 12
  6. 2023Dialogue Games for Benchmarking Language Understanding: Motivation, Taxonomy, Strategycited by 12
  7. 2023Dealing with Semantic Underspecification in Multimodal NLPcited by 9
  8. 2023What A Situated Language-Using Agent Must be Able to Do: A Top-Down Analysiscited by 8
  9. 2024Conversational Grounding: Annotation and Analysis of Grounding Acts and Grounding Unitscited by 8
  10. 2022The slurk Interaction Server Framework: Better Data for Better Dialog Modelscited by 7
  11. 2023Resolving References in Visually-Grounded Dialogue via Text Generationcited by 5
  12. 2025Understanding Common Ground Misalignment in Goal-Oriented Dialog: A Case-Study with Ubuntu Chat Logscited by 4
7 more
  1. 2025A Third Paradigm for LLM Evaluation: Dialogue Game-Based Evaluation using clembenchcited by 3
  2. 2024Using Game Play to Investigate Multimodal and Conversational Grounding in Large Multimodal Modelscited by 2
  3. 2023Collecting Visually-Grounded Dialogue with A Game Of Sortscited by 1
  4. 2026Frame of Reference: Addressing the Challenges of Common Ground Representation in Situational Dialogscited by 1
  5. 2021We went to look for meaning and all we got were these lousy representations: aspects of meaning representation for computational semanticscited by 0
  6. 2022Spot the Difference: A Cooperative Object-Referring Game in Non-Perfectly Co-Observable Scenecited by 0
  7. 2026Using Machine Mental Imagery for Representing Common Ground in Situated Dialoguecited by 0