Documentation

Linglib.Phenomena.Presupposition.Studies.HeKaiserIskarous2025

Sentence Polarity Asymmetries #

@cite{he-kaiser-iskarous-2025}

Empirical data and domain types for modeling sentence polarity asymmetries with fuzzy interpretations in a possibly wonky world.

Key Phenomena #

Two asymmetries between positive and negative polarity:

  1. Cost asymmetry: Negation elicits higher production cost than positive polarity
  2. Presupposition asymmetry: Negation presupposes prominence of its positive counterpart in common ground, but not vice versa

Domain #

Part-whole relations (e.g., house-bathroom, classroom-stove):

Models #

ModelDescription
Standard RSABaseline with Boolean semantics
fuzzyRSASoft semantics with polarity-specific interpretation
wonkyRSAComplex prior for common ground update
funkyRSACombination of fuzzy + wonky

Two states for part-whole relations.

  • pos: The positive state (A has B)
  • neg: The negative state (A doesn't have B)
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      All states

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        Three utterances for part-whole communication.

        • uPos: "A has B" (positive polarity, cost 1)
        • uNeg: "A doesn't have B" (negative polarity, cost 2)
        • uNull: Say nothing (cost 0)
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            All utterances

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              Sentence polarity

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                  Boolean literal semantics: which utterance is true in which state.

                  • u_pos is true only in s_pos
                  • u_neg is true only in s_neg
                  • u_null is true in both (vacuously)
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                    World types for wonkyRSA.

                    • normal: Prior reflects actual world knowledge
                    • wonky: Uniform prior (speaker's utterance choice is "odd")
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                        All world types

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                          Prior probability over states.

                          In the He et al. study, priors were normed empirically for 81 part-whole pairs (e.g., house-bathroom has high prior, classroom-stove has low prior).

                          • p_pos :

                            P(s_pos) - probability of positive state

                          • h_pos_nonneg : 0 self.p_pos

                            Non-negative

                          • h_pos_le_one : self.p_pos 1

                            At most 1

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                            P(s_neg) = 1 - P(s_pos)

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                              Uniform prior: P(s_pos) = P(s_neg) = 0.5

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                                High prior: P(s_pos) = 0.9 (typical part, e.g., house-bathroom)

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                                  Low prior: P(s_pos) = 0.1 (atypical part, e.g., classroom-stove)

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                                    Parameters for fuzzy interpretation functions.

                                    For negative utterances: constant probability n For positive utterances: sigmoid function with parameters (L, k, x0, c)

                                    • n :

                                      Negative interpretation: [u_neg] = n

                                    • L :

                                      Sigmoid maximum value

                                    • k :

                                      Sigmoid steepness

                                    • x0 :

                                      Sigmoid midpoint

                                    • c :

                                      Sigmoid vertical shift

                                    • h_n_valid : 0 self.n self.n 1

                                      n in [0,1]

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                                      Best-fit parameters from the paper (Section 4.2).

                                      n = 0.8, α = 1, θ = {L=0.7, k=6, x0=0.35, c=0.3}

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                                        Configuration for RSA model instances

                                        • prior : HKIPrior

                                          Prior over states

                                        • alpha :

                                          Rationality parameter

                                        • p_wonky :

                                          Wonkiness prior P(wonky) for wonkyRSA

                                        • fuzzyParams : FuzzyParams

                                          Fuzzy parameters for fuzzyRSA

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                                          Asymmetry Hypothesis 1: Negation has higher production cost.

                                          Marked forms like negation use more complex structures and longer linguistic forms, eliciting higher production cost.

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                                            Asymmetry Hypothesis 2: Negation presupposes positive prominence.

                                            Negation presupposes that its positive-polarity counterpart is relevant or prominent in common ground, but not vice versa.

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                                              Simple sigmoid approximation using rational arithmetic.

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                                                Fuzzy interpretation for positive utterances.

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                                                  Combined fuzzy meaning function for fuzzyRSA.

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                                                    A simple model for part-whole relations.

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                                                        The "has" relation: which containers have which parts.

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                                                          Key theorem: negative meaning is compositionally derived via neg.

                                                          Negative sentence meaning as world-indexed proposition. ⟦"A doesn't have B"⟧ = pnot(⟦"A has B"⟧)

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