Knowledge and implicature: Modeling language understanding as social cognition

By Noah D. Goodman, Andreas Stuhlmüller. 2012

419 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cultural Studies, Developmental and Educational Psychology, Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Cultural Studies

In Developmental and Educational Psychology

In Natural Language Processing

Citations in the corpus

GeDi: Generative Discriminator Guided Sequence Generation (2020, background): “GeDi also relates to the rational speech acts framework for computational pragmatics (Frank and Goodman, 2012; Goodman and Stuhlmüller, 2013) where a “listener” model and a “speaker” model interactively generate a sequence such that the listener can recover the input.”
Speaker-Follower Models for Vision-and-Language Navigation (2018, methodology): “Drawing on the Rational Speech Acts framework [14, 17], a pragmatic follower model should choose a route r through the environment that has high probability of having caused the speaker model to produce the given description d: argmaxr PS(d | r) (corresponding to a rational Bayesian follower with a ”
Asking for Help Using Inverse Semantics (2014, background): “However, if a person is unfamiliar with the robotic system and not knowledgeable about its capabilities and limitations, they might not know how to help the robot recover from a failure.”
Reasoning about reasoning by nested conditioning: Modeling theory of mind with probabilistic programs (2014, result): “This effect has been confirmed experimentally in Goodman and Stuhlmüller [12].”
Concepts in a Probabilistic Language of Thought (2014, background): “As described elsewhere (M. Frank & Goodman, 2012; Goodman & Lassiter, to appear; Goodman & Stuhlmüller, 2013; Stuhlmüller & Goodman, 2013) a broad range of pragmatic inferences can be understood as the output of conditioning in an intuitive theory of communication, and can also be formalized using…”
Implicit Communication of Actionable Information in Human-AI teams (2019, neutral): “More tractable techniques isolate specific and far more restricted types of implicature, such as the technique proposed for scalar implicature [31].”
Inferring Rewards from Language in Context (2022, background): “…(Golland et al., 2010; Frank and Goodman, 2012; Degen et al., 2013; Monroe et al., 2017; McDowell and Goodman, 2019), interpreting implicatures (Goodman and Stuhlmüller, 2013; Bergen et al., 2016), or generating (Fried et al., 2018a; Sumers et al., 2021) or interpreting grounded instructions…”
Colors in Context: A Pragmatic Neural Model for Grounded Language Understanding (2017, background): “The model and its variants have been shown to capture a wide range of pragmatic phenomena in a cognitively realistic manner (Goodman and Stuhlmüller, 2013; Smith et al., 2013; Kao et al., 2014; Bergen et al., 2016), and the central Bayesian calculation has proven useful in a variety of…”
Context, scale structure, and statistics in the interpretation of positive-form adjectives (2013, methodology): “We follow closely recent work in Bayesian pragmatics (Frank & Goodman 2012; Bergen, Goodman & Levy 2012; Goodman & Stuhlmüller 2012; Smith, Goodman & Frank 2013; Goodman & Lassiter 2014), which combine Gricean and game-theoretic influences with an approach to inference and decision-making under…”
Pragmatic Language Interpretation as Probabilistic Inference. (2016, positive): “One study [32] measured participants’ judgments about the interpretations of quantifiers and number words in exactly this situation and found that these judgments were well-predicted by RSA.”
Pragmatically Informative Image Captioning with Character-Level Inference (2018, methodology): “In this paper, we present a neural image captioning system 1 that is a pragmatic speaker as deﬁned by the Rational Speech Acts (RSA) model (Frank and Goodman, 2012; Goodman and Stuhlm¨uller, 2013).”
Semantic Communications for Future Internet: Fundamentals, Applications, and Challenges (2022, neutral): “In linguistics, contextual reasoning is often computationally described using the rational speech act model [118], [119], [120], which is rooted in the Gricean view of language use [121].”
Why discourse affects speakers’ choice of referring expressions (2015, background): “, 2013), and studies report that they robustly predict various linguistic phenomena in experimental settings (Goodman and Stuhlmüller, 2013; Degen et al., 2013; Kao et al., 2014; Nordmeyer and Frank, 2014).”
Children interpret disjunction as conjunction: Consequences for theories of implicature and child development (2016, neutral): “…Gibson 1998, Gibson 2000, amont others), plausibility judgments (e.g., Crain and Steedman 1985, Trueswell et al. 1994, Stolcke 1995, Jurafsky 1996, Goodman and Stuhlmüller 2013, Bergen and Grodner 2012, among others), preferences for parses that best answer a Question Under Discussion (e.g.,…”
Investigating the distribution of some (but not all ) implicatures using corpora and web-based methods (2015, background): “However, recent work (Degen et al. 2013, Frank & Goodman 2012, Goodman & Stuhlmüller 2013) has provided evidence that scalar implicature is a probabilistic phenomenon; treating the process of interpreting an utterance with some as more or less strongly giving rise to the belief that the stronger…”
Reward-rational (implicit) choice: A unifying formalism for reward learning (2020, background): “Boltzmann-rational policies are widespread in psychology [13, 14, 15], economics [16, 17, 18, 17], and AI [19, 20, 21, 22, 23] as models of human choices, actions, or inferences.”