Documentation

Linglib.Studies.Warstadt2022

[War22]: Presupposition Triggering and Utterance Utility [War22] #

Empirical domain types and truth conditions for Warstadt's genus-species presupposition model. Two examples demonstrate that presupposition triggering emerges from pragmatic reasoning about utterance utility.

Green Card Example (Table 1) #

Three worlds, five utterances, two QUDs. The central prediction: under the "need visa?" QUD, "not green card" triggers the genus inference (Tom is non-US), but under "free drink?" QUD, no such inference arises.

Family-Genus-Species Example (Table 2) #

Four worlds in a taxonomic hierarchy (Olympic sprinter ⊂ runner ⊂ athlete), seven utterances, non-uniform priors. Species-level negation ("not Olympic sprinter") triggers stronger accommodation than genus-level ("not runner").

Green Card Example (Table 1) #

World states for the green card scenario.

  • usCitizen: Tom is a US citizen (no green card possible)
  • gcHolder: Tom is a non-US citizen with a green card
  • nonUS: Tom is a non-US citizen without a green card
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    def Warstadt2022.instReprGCWorld.repr :
    GCWorldStd.Format
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      Assertable content for the green card scenario (silence is added separately via RSA.WithSilence).

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          Utterances for the green card scenario: none is the null utterance, some a a paper-utterance.

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            QUDs for the green card scenario.

            • needVisa: Does Tom need a visa? Partition: {usCitizen, gcHolder} vs {nonUS}
            • freeDrink: Can Tom get a free drink? Partition: {gcHolder} vs {usCitizen, nonUS}
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              def Warstadt2022.instReprGCQUD.repr :
              GCQUDStd.Format
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                Utterance-level meaning: silence is universally true.

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                  def Warstadt2022.gcQUDProject (q : GCQUD) (w1 w2 : GCWorld) :
                  Bool

                  QUD projection: two worlds are equivalent iff they give the same QUD answer.

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                    Uniform world prior.

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                      PartialProp decomposition of "green card": presupposes non-US, asserts has GC.

                      This captures the traditional presupposition analysis. The paper's key contribution is showing that this presupposition structure EMERGES from RSA reasoning over Boolean truth conditions, without being stipulated.

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                        The Boolean meaning of "green card" decomposes as presupposition ∧ assertion.

                        "not green card" is Boolean negation of "green card".

                        "not US" is Boolean negation of "US".

                        Family-Genus-Species Example (Table 2) #

                        World states for the family-genus-species hierarchy.

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                          def Warstadt2022.instReprFGSWorld.repr :
                          FGSWorldStd.Format
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                            Assertable content for the family-genus-species scenario (silence is added separately via RSA.WithSilence).

                            Six utterances: three positive descriptions at each taxonomic level plus their Boolean negations.

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                                Utterances for the family-genus-species scenario: none is the null utterance, some a a paper-utterance.

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                                    Truth conditions from Table 2.

                                    Respects the taxonomic hierarchy: Olympic sprinter ⊂ runner ⊂ athlete.

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                                      Utterance-level meaning: silence is universally true.

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                                        Max QUD (full world identification).

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                                          Olympic sprinter entails runner.

                                          Runner entails athlete.

                                          Olympic sprinter entails athlete (transitivity).

                                          Boolean negation: not Olympic sprinter = ¬ Olympic sprinter.

                                          Boolean negation: not runner = ¬ runner.

                                          Boolean negation: not athlete = ¬ athlete.

                                          Green Card: Context Types #

                                          A context is a subset of GCWorlds.

                                          • usCitizen : Bool
                                          • gcHolder : Bool
                                          • nonUS : Bool
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                                            def Warstadt2022.instDecidableEqGCContext.decEq (x✝ x✝¹ : GCContext) :
                                            Decidable (x✝ = x✝¹)
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                                                All 2³ = 8 contexts (subsets of GCWorld).

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                                                    Family-Genus-Species: Context Types #

                                                    A context is a subset of FGSWorlds.

                                                    • olympicSprinter : Bool
                                                    • runner : Bool
                                                    • otherAthlete : Bool
                                                    • nonAthlete : Bool
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                                                      def Warstadt2022.instDecidableEqFGSContext.decEq (x✝ x✝¹ : FGSContext) :
                                                      Decidable (x✝ = x✝¹)
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                                                          All 2⁴ = 16 contexts.

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                                                                def Warstadt2022.instReprFGSQUD.repr :
                                                                FGSQUDStd.Format
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                                                                  PartialProp Connection #

                                                                  The Boolean meaning of "green card" decomposes as presupposition ∧ assertion.

                                                                  "not green card" is Boolean negation — no presupposition in the semantics.