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Pragmatics in Language Grounding: Phenomena, Tasks, and Modeling Approaches

Summary

This survey examines how NLP systems, including LLMs, can handle pragmatics: the way people use context to convey more than they literally say. The authors argue that current models rarely have direct access to the relevant context, such as the physical world, the interaction history, or the effects of utterances. As a result, they still fail on many non-literal phenomena. The paper sets out a taxonomy of context types and of the roles pragmatic reasoning plays. It then reviews grounded tasks and datasets, ordered from simple reference games to embodied, goal-oriented dialogue, and describes how design choices shape the pragmatic behaviour each task elicits. It also covers modelling frameworks, such as RSA, that treat communication as goal-directed action by agents. The main finding is that richer settings, especially those with partial observability or symmetric roles, bring out more pragmatic phenomena. The authors recommend designing tasks with richer context and evaluating systems on efficiency, non-literal understanding and convention formation, not only on task success.

Key points

  • Six context types: social and communicative norms, task goals and collaborative actions, common knowledge, discourse context, visual information, and embodied interaction. The first four belong to common ground.
  • Five roles of pragmatics: reasoning about alternatives, resolving ambiguity, collaborative planning, convention formation and abstraction, and efficiency through mutual exclusivity.
  • Four task families: reference games (e.g., ReferItGame), image captioning, instruction following (e.g., CerealBar) and grounded dialogue. Each is compared on partial observability, symmetry and iterated interaction.
  • LLMs inside pragmatic models: Liu et al. (2023) pair RSA with meta-learning to use GPT models in image reference games. FAIR et al. (2022) combine BART with multi-agent planning for the game Diplomacy.
  • Open challenges: evaluation, where human judgement remains the gold standard and self-play serves as a proxy, and data sparsity, caused by multimodal conditioning, domain-specific action spaces and adaptation to individual users. Unimodal, synthetic and emergent-language work is explicitly out of scope.

Our summary of the paper, in our own words β€” not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages

Cited by

25 papers of our library cite it (of the 140673 whose bibliographies we read); 2 of them have a page here, 23 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2023.findings-emnlp.840 Β· arXiv 2211.08371

@inproceedings{fried2023pragmatics,
  title = {{Pragmatics in Language Grounding: Phenomena, Tasks, and Modeling Approaches}},
  author = {Fried, Daniel and Tomlin, Nicholas and Hu, Jennifer and Patel, Roma and Nematzadeh, Aida},
  booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2023},
  year = {2023},
  doi = {10.18653/v1/2023.findings-emnlp.840},
  eprint = {2211.08371},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/2023.findings-emnlp.840}
}

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