Pragmatic reasoning through semantic inference

By Leon Bergen, Roger Levy, Noah D. Goodman. 2016

165 citations recorded by the source corpus.

A number of recent proposals have used techniques from game theory and Bayesian cognitive science to formalize Gricean pragmatic reasoning (Frank & Goodman, 2012; Franke, 2009; Goodman & Stuhlmuller, 2013; Jager, 2012). We discuss two phenomena which pose a challenge to these accounts of pragmatics: M-implicatures (Horn, 1984) and embedded implicatures which violate Hurford’s constraint (Chierchia, Fox, & Spector, 2012; Hurford, 1974). While techniques have been developed for deriving M-implicatures, Hurford-violating em- bedded implicatures pose a more fundamental challenge, because of basic limitations in the models’ architecture. In order to explain these phenomena, we propose a realignment of the division between semantic content and pragmatic content. Under this proposal, the semantic content of an utterance is not fixed independent of pragmatic inference; rather, pragmatic inference partially determines an utterance’s semantic content. We show how semantic inference can be realized as an extension to the Rational Speech Acts framework (Goodman & Stuhlmuller, 2013). The addition of lexical uncertainty derives both M-implicatures and the relevant embedded implicatures, and preserves the derivations of more standard implicatures. We use this principle to explain a novel class of implicature, non-convex disjunctive implicatures, which have several theoretically interesting properties. In particular, these implicatures can be preserved in downward-entailing contexts in the absence of accenting, a property which is predicted by lexical uncertainty, but which violates prior generalizations in the literature (Fox & Spector, in press; Horn, 1989). EARLY ACCESS VERSION

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

Experience Grounds Language (2020, methodology): “A.M. Turing (Computing Machinery and Intelligence 1950) In the language community, this paradigm has been described under the “Speaker-Listener” model (Stephens et al., 2010), and a rich theory to describe this computationally is being actively developed under the Rational Speech Act Model (Frank an”
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., 2014), and the central Bayesian calculation has proven useful in a variety of communicative”
Pragmatic Language Interpretation as Probabilistic Inference. (2016, unknown): “This extension yields effects similar to Grice’s maxim of manner [9].”
Continual Adaptation for Efficient Machine Communication (2019, background): “However, because θi is not directly observable and θ varies across partners and contexts, it must be inferred (Bergen et al., 2016).”
Inferring Rewards from Language in Context (2022, methodology): “…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 (Fried et al., 2018b).”