mlsanityData to 7 Oct 2026

Dialogue Games for Benchmarking Language Understanding: Motivation, Taxonomy, Strategy

arXiv 2304.07007 ↗First posted 14 Apr 2023cs.CLcited by 12

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Abstract (arXiv)

How does one measure "ability to understand language"? If it is a person's ability that is being measured, this is a question that almost never poses itself in an unqualified manner: Whatever formal test is applied, it takes place on the background of the person's language use in daily social practice, and what is measured is a specialised variety of language understanding (e.g., of a second language; or of written, technical language). Computer programs do not have this background. What does that mean for the applicability of formal tests of language understanding? I argue that such tests need to be complemented with tests of language use embedded in a practice, to arrive at a more comprehensive evaluation of "artificial language understanding". To do such tests systematically, I propose to use "Dialogue Games" -- constructed activities that provide a situational embedding for language use. I describe a taxonomy of Dialogue Game types, linked to a model of underlying capabilites that are tested, and thereby giving an argument for the construct validity of the test. I close with showing how the internal structure of the taxonomy suggests an ordering from more specialised to more general situational language understanding, which potentially can provide some strategic guidance for development in this field.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 26 of its 67 references that are in the map. First posted on arXiv 14 Apr 2023.

Builds on

26 of 67 references.

References · 26

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14 more
  1. 2016Multi-Agent Cooperation and the Emergence of (Natural) Languagecited by 181
  2. 2022Language Models as Agent Modelscited by 131
  3. 2022Vision-and-Language Navigation: A Survey of Tasks, Methods, and Future Directionscited by 116
  4. 2017Learning Cooperative Visual Dialog Agents with Deep Reinforcement Learningcited by 70
  5. 2021MindCraft: Theory of Mind Modeling for Situated Dialogue in Collaborative Taskscited by 62
  6. 2020Targeting the Benchmark: On Methodology in Current Natural Language Processing Researchcited by 44
  7. 2022Abstract Visual Reasoning with Tangram Shapescited by 43
  8. 2019MeetUp! A Corpus of Joint Activity Dialogues in a Visual Environmentcited by 19
  9. 2022Multimodal Conversational AI: A Survey of Datasets and Approachescited by 16
  10. 2019Grounded Agreement Games: Emphasizing Conversational Grounding in Visual Dialogue Settingscited by 10
  11. 2023What A Situated Language-Using Agent Must be Able to Do: A Top-Down Analysiscited by 8
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  14. 2022Can Visual Dialogue Models Do Scorekeeping? Exploring How Dialogue Representations Incrementally Encode Shared Knowledgecited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 4; 2025: 4; 2026: 1.

12 papers.