mlsanityData to 7 Oct 2026

Analysis of Language Change in Collaborative Instruction Following

arXiv 2109.04452 ↗First posted 9 Sep 2021cs.CLcited by 9

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Abstract (arXiv)

We analyze language change over time in a collaborative, goal-oriented instructional task, where utility-maximizing participants form conventions and increase their expertise. Prior work studied such scenarios mostly in the context of reference games, and consistently found that language complexity is reduced along multiple dimensions, such as utterance length, as conventions are formed. In contrast, we find that, given the ability to increase instruction utility, instructors increase language complexity along these previously studied dimensions to better collaborate with increasingly skilled instruction followers.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 9 of its 21 references that are in the map. First posted on arXiv 9 Sep 2021.

Builds on

9 of 21 references.

References · 9

  1. 2019Executing Instructions in Situated Collaborative Interactionscited by 68
  2. 2017Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddingscited by 59
  3. 2017Colors in Context: A Pragmatic Neural Model for Grounded Language Understandingcited by 57
  4. 2019The PhotoBook Dataset: Building Common Ground through Visually-Grounded Dialoguecited by 46
  5. 2019A Natural Language Corpus of Common Grounding under Continuous and Partially-Observable Contextcited by 35
  6. 2019Characterizing the dynamics of learning in repeated reference gamescited by 34
  7. 2017Naturalizing a Programming Language via Interactive Learningcited by 20
  8. 2020Generalizing meanings from partners to populations: Hierarchical inference supports convention formation on networkscited by 4
  9. 2018Precise but Natural Specification for Robot Taskscited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 1; 2024: 4; 2025: 0; 2026: 1.

9 papers.