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Cited by 26Syntax, Semantics, Linguistic VariationLogic, Reasoning, and KnowledgeLanguage Development and Disorders

An Incremental Iterated Response Model of Pragmatics

Open access
Cohn-Gordon, Reuben, Goodman, Noah, Potts, Christopher·2019·University of Massachusetts Amherst
DOI 10.7275/cprc-8x17
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Abstract

Recent Iterated Response (IR) models of pragmatics conceptualize language use as a recursive process in which agents reason about each other to increase communicative efficiency. These models are generally defined over complete utterances. However, there is substantial evidence that pragmatic reasoning takes place incrementally during production and comprehension. We address this with an incremental IR model. We compare the incremental and global versions using computational simulations, and we assess the incremental model against existing experimental data and in the TUNA corpus for referring expression generation, showing that the model can capture phenomena out of reach of global versions.

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  • Modeling pragmatic agents based on neural network architectures can help to scale the applicability of the RSA framework.

    “Modeling pragmatic agents based on neural network architectures can help to scale the applicability of the RSA framework [56,65–67].”

    From The role of pragmatic mechanisms in referential communication and categorization: An emergent communication model (2026) · cites this paper as [67]

    Section 5 (p. 8) says an incremental RSA approach offers a scalable alternative if its incremental semantics is learned. Section 2.4 (p. 5) clarifies that the model presented here is not itself more tractable, and our copy does not specify neural-network architectures as the basis for scaling it. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • Neural-network-based approaches for RSA-based referring expression generation approximate the inference, for example by directly taking into account the interpretation of an internal listener model.

    “Instead of the computationally costly Bayesian inference in the classical RSA paradigm, neural-network-based approaches for RSA-based referring expression generation approximate the inference, for example by directly taking into account the interpretation of an internal listener model [56,57,65–67].”

    From The role of pragmatic mechanisms in referential communication and categorization: An emergent communication model (2026) · cites this paper as [67]

    Section 2.1 (p. 2) says the RSA speaker reasons about a listener’s interpretation, and Section 2.3 (p. 4) describes efficient, word-by-word referring-expression generation. But the paper presents an incremental RSA model, not a neural-network-based approach that approximates classical RSA inference; Section 2.4 (p. 5) describes its predictions as distinct from the global model’s. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

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Flagged first, then by citations

Research works printed but not linked: 18

  1. Reuben Cohn-Gordon, Noah Goodman, and Christopher Potts. 2018. Pragmatically informative image captioning with character-level inference. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers) , pages 439-443. Association for Computational Linguistics.
  2. Kees van Deemter, Ielka van der Sluis, and Albert Gatt. 2006. Building a semantically transparent corpus for the generation of referring expressions. In Proceedings of the Fourth International Natural Language Generation Conference , pages 130-132. Association for Computational Linguistics.
  3. Paul E Engelhardt, Karl GD Bailey, and Fernanda Ferreira. 2006. Do speakers and listeners observe the gricean maxim of quantity? Journal of Memory and Language , 54(4):554-573.
  4. Michael Franke. 2009. Signal to Act: Game Theory in Pragmatics . ILLC Dissertation Series. Institute for Logic, Language and Computation, University of Amsterdam.
  5. Michael Franke and Gerhard J¨ ager. 2014. Pragmatic back-and-forth reasoning. In Salvatore Pistoia Reda, editor, Pragmatics, Semantics and the Case of Scalar Implicatures , pages 170-200. Palgrave Macmillan UK, London.
  6. Albert Gatt, Anja Belz, and Eric Kow. 2009. The TUNA-REG Challenge 2009: Overview and evaluation results. In Proceedings of the 12th European Workshop on Natural Language Generation (ENLG 2009) , pages 174-182, Athens, Greece. Association for Computational Linguistics.
  7. Noah D Goodman and Michael C Frank. 2016. Pragmatic language interpretation as probabilistic inference. Trends in Cognitive Sciences , 20(11):818829.
  8. Noah D. Goodman and Andreas Stuhlm¨ uller. 2013. Knowledge and implicature: Modeling language understanding as social cognition. Topics in Cognitive Science , 5(1):173-184.
  9. H. Paul Grice. 1975. Logic and conversation. In Peter Cole and Jerry Morgan, editors, Syntax and Semantics , volume 3: Speech Acts, pages 43-58. Academic Press, New York.
  10. Gerhard J¨ ager. 2007. Game dynamics connects semantics and pragmatics. In Ahti-Veikko Pietarinen, editor, Game Theory and Linguistic Meaning , pages 89-102. Elsevier, Amsterdam.
  11. Gerhard J¨ ager. 2012. Game theory in semantics and pragmatics. In Claudia Maienborn, Klaus von Heusinger, and Paul Portner, editors, Semantics: An International Handbook of Natural Language Meaning , volume 3, pages 2487-2425. Mouton de Gruyter, Berlin.
  12. Justine T. Kao, Jean Y. Wu, Leon Bergen, and Noah D. Goodman. 2014. Nonliteral understanding of number words. Proceedings of the National Academy of Sciences , 111(33):12002-12007.
  13. Will Monroe and Christopher Potts. 2015. Learning in the Rational Speech Acts model. In Proceedings of 20th Amsterdam Colloquium , Amsterdam. ILLC.
  14. Christopher Potts, Daniel Lassiter, Roger Levy, and Michael C. Frank. 2016. Embedded implicatures as pragmatic inferences under compositional lexical uncertainty. Journal of Semantics , 33(4):755-802.
  15. Julie C Sedivy. 2007. Implicature during real time conversation: A view from language processing research. Philosophy compass , 2(3):475-496.
  16. Ramakrishna Vedantam, Samy Bengio, Kevin Murphy, Devi Parikh, and Gal Chechik. 2017. Context-aware captions from context-agnostic supervision. In Computer Vision and Pattern Recognition (CVPR) , volume 3.
  17. Adam Vogel, Max Bodoia, Christopher Potts, and Dan Jurafsky. 2013. Emergence of Gricean maxims from multi-agent decision theory. In Human Language Technologies: The 2013 Annual Conference of the North American Chapter of the Association for Computational Linguistics , pages 10721081, Stroudsburg, PA. Association for Computational Linguistics.
  18. Erica J. Yoon, Michael Henry Tessler, Noah D. Goodman, and Michael C. Frank. 2016. Talking with tact: Polite language as a balance between kindness and informativity. In Proceedings of the 38th Annual Conference of the Cognitive Science Society , pages 2771-2776. Cognitive Science Society.

Cited by

Sorted by citations

Authors
Cohn-Gordon, R., Goodman, N., & Potts, C.
Affiliations
Stanford University (US)
ORCID
Venue
University of Massachusetts Amherst
Publisher
University of Massachusetts Amherst
Published
2019
Volume
2
Issue
1
Pages
81-90
ISSN
2331-8422 (ISSN-L)
DOI
10.7275/cprc-8x17
OpenAlex
W2962955572
Cited by
26
References
3
Citations by year
2026: 2 · 2023: 5 · 2022: 1 · 2021: 8 · 2020: 8 · 2019: 2
Type
posted content
Language
en
Open access
Locations
  • arXiv (Cornell University) (submittedVersion)
  • ScholarWorks@UMassAmherst (University of Massachusetts Amherst) (submittedVersion)
  • arXiv (Cornell University)
  • University of Massachusetts (UMass) Amherst
Funders
National Science Foundation (1456077)
Topics
  • Social Sciences › Arts and Humanities › Language and Linguistics › Syntax, Semantics, Linguistic Variation
  • Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
  • Social Sciences › Psychology › Developmental and Educational Psychology › Language Development and Disorders
Keywords
pragmatics, incremental reasoning, pragmatic reasoning, referring expressions, utterance production, utterance interpretation, computer simulation, recursive reasoning

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