Documentation

Linglib.Phenomena.ScalarImplicatures.Studies.FrankeBergen2020

@cite{franke-bergen-2020} — GI-RSA for Nested Aristotelians #

@cite{franke-bergen-2020} @cite{fox-2007} @cite{franke-2011}

"Theory-driven statistical modeling for semantics and pragmatics: A case study on grammatically generated implicature readings"

The Model #

Domain: Nested Aristotelian sentences "Q₁ of the aliens drank Q₂ of their water" with Q₁, Q₂ ∈ {none, some, all}. 7 world states based on which alien types exist (none-drinkers, some-drinkers, all-drinkers). 8 grammatical parses (subsets of {M, O, I} EXH positions) as latent variables.

The Global Intentions (GI) model treats the parse as a latent variable: the speaker chooses both an utterance and a parse, the listener marginalizes over parses to recover the world state.

Exhaustification uses compositional enrichment at inner/outer quantifier positions: Exh(Q) conjoins Q with the negation of all strictly stronger lexical alternatives on the {some, all} scale (@cite{fox-2007}). Only Exh(some) = "some but not all" is non-vacuous; Exh(all) and Exh(none) have no strictly stronger alternative.

At matrix position, EXH uses exh_MW (eq. A2): negate the LITERAL meanings of sentential alternatives that are strictly stronger than the sub-matrix enriched meaning, with a noncontradictory fallback. Sentential alternatives are the cross-product of scale-mate substitutions ({some, all}) for each on-scale quantifier. "none" is treated as off-scale; the paper notes (fn. 7) that including "not all" as an alternative to "none" has negligible effect on predictions.

Parameters: α = 2 (modeling choice; the paper estimates α jointly with cost and error parameters, reporting MLE ≈ 1.3 for GI), uniform priors. The paper's cost term c(u) for "none"-initial utterances and error term ε are omitted; the qualitative predictions below are robust to these simplifications.

Qualitative Findings #

#FindingTheorem
1SS exhaustifies inner Qss_inner_exh
2SS exhaustifies outer Qss_outer_exh
3AA identifies unique worldaa_identifies
4AS exhaustifies inner Qas_inner_exh
5Full EXH preferred for SSss_parse_pref
6Vanilla gets SS wrong (prefers wNA)vanilla_ss_prefers_wNA
7GI gets SS right (prefers wNS)gi_ss_prefers_wNS
8LU gets SS→wNS rightlu_ss_prefers_wNS
9LI derives outer exh for SSli_ss_outer_exh
10LI also gets SS→wNS rightli_ss_prefers_wNS

Model Comparison #

All four models from the paper are formalized as RSAConfig instances differing only in Latent type:

Bayesian model comparison: GI achieves 0.956 posterior probability, LI = 0.033, LU = 0.010, vanilla = 0.

Aristotelian quantifiers: none, some, all.

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      The 7 world states, distinguished by which alien types exist. Three types: N (drank none), S (drank some but not all), A (drank all). Worlds are the 7 non-empty subsets of {N, S, A}.

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          The 9 nested Aristotelian utterances: Q_outer × Q_inner.

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              The 8 grammatical parses: subsets of {M, O, I} EXH positions.

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                  Construct an utterance from outer × inner quantifiers.

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                    Outer quantifier evaluation: does the world satisfy "Q_outer of the aliens [satisfy inner VP]"? nSat, sSat, aSat indicate which alien types satisfy the inner VP.

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                      Literal meaning: compose inner VP satisfaction with outer quantifier.

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                        All 7 worlds for enumeration.

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                          Inner VP satisfaction after EXH enrichment. Only "some" is enrichable: Exh(some) = "some but not all" — satisfied by S-types only (not A-types). Exh(none) and Exh(all) are vacuous (no strictly stronger alternative).

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                            The lexical scale: {some, all}.

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                              Sentential alternatives at matrix position: cross-product of scale-mate substitutions for each scalar item in the utterance. Non-scalar positions are held fixed.

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                                Exhaustified meaning under a given parse, using compositional enrichment.

                                Inner/outer EXH enrich quantifiers in situ (at the quantifier level, not the sentence level), following the paper's Appendix. Only "some" → "some but not all" is a meaningful enrichment; Exh(all) and Exh(none) are vacuous (no strictly stronger lexical alternative on the scale).

                                Matrix EXH uses exh_MW with a strength filter: negate the LITERAL meanings of sentential alternatives that are strictly stronger than the enriched (sub-matrix) meaning. Fall back to no matrix EXH if the conjunction is contradictory (eq. A2).

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                                  EXH_I(SS) = worlds with S-types (compositional: "some aliens drank some but not all"). Inner EXH enriches "some" to "some but not all", so S-types satisfy the VP but A-types do not. The outer "some" requires at least one S-type, giving {wNS, wNSA, wS, wSA}.

                                  EXH_OI(SS) = {wNS, wNSA, wSA} — outer "some but not all" alien types satisfy the enriched inner predicate. wS excluded because at wS all types (just S) satisfy the inner pred, so "all" would be true.

                                  EXH_MOI(SS) = EXH_OI(SS) = {wNS, wNSA, wSA} — matrix EXH is vacuous here because no sentential alternative is strictly stronger than the OI-enriched meaning.

                                  GI-RSA model for nested Aristotelians. Parse is the latent variable; meaning under each parse is determined by compositional exhaustification. α = 2 is a modeling choice for qualitative predictions (the paper estimates α ≈ 1.3 jointly with cost and error parameters).

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                                    SS exhaustifies inner quantifier: L1 prefers wNS (N and S types, no A) over wNSA (all three types including A). Inner EXH negates "some drank all", disfavoring worlds with A-type aliens.

                                    SS exhaustifies outer quantifier: L1 prefers wNS (mixed N+S types) over wS (only S-type). At wS, "all aliens drank some but not all" would be true, but outer EXH negates the "all" reading, preferring worlds where not all alien types are uniform.

                                    AS exhaustifies inner: L1 prefers wS (all aliens are "some" drinkers) over wA (all aliens are "all" drinkers).

                                    Full exhaustification is preferred: L1 assigns more probability to the fully exhaustified parse (MOI) than to the literal parse for SS.

                                    Vanilla RSA (§3.1): literal semantics only, no grammatical enrichment. Each utterance has a single reading; no latent parse variable.

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                                      LU lexicon: literal or OI (inner + outer EXH). Each speaker has a fixed lexicon; listeners marginalize over the two lexica to infer the world state. Mirrors the @cite{potts-etal-2016} architecture (Latent := Lexicon).

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                                          Lexical Uncertainty model (§3.2): speaker has a fixed lexicon (lit or OI), listener marginalizes over lexica. Simplified to L1 — the paper adds an S2/L2 layer (eqs. 14a-14b) for production predictions.

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                                            LI parse: lit, I, O, or OI. The speaker actively chooses a parse per utterance (unlike LU, where each speaker has a fixed lexicon).

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                                                Lexical Intentions model (§3.3): speaker chooses (utterance, parse) jointly from {lit, I, O, OI}, listener marginalizes over parses.

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                                                  The paper's key empirical finding: GI predicts SS→wNS (the state where N-types and S-types coexist), matching production data. Vanilla RSA cannot access the exhaustified reading ⟦SS⟧^MOI = {wNS, wNSA, wSA} and instead predicts wNA (N+A types) for hearing SS.

                                                  Vanilla RSA hearing SS: prefers wNA over wNS. Without exhaustification, SS is literally true at 6/7 worlds. At wNA, the competing utterances (SN, SA) are less informative than at wNS (where NA is a strong competitor), so SS "wins" more of the S1 score at wNA.

                                                  This is the wrong prediction — production data shows SS is used for state wNS, not wNA. Evidence for grammatical enrichment.

                                                  GI correctly predicts the opposite: SS→wNS, not wNA. The exhaustified parses concentrate probability on worlds with S-types but not A-types, overriding the literal-meaning signal.

                                                  LU also gets the key SS prediction right: wNS > wNA. The OI lexicon gives ⟦SS⟧^OI = {wNS, wNSA, wSA}, which excludes wNA. The lit lexicon includes both, but OI's exclusion of wNA is enough to tip the balance. Same architecture as @cite{potts-etal-2016} (Latent := Lexicon).

                                                  LI derives outer exhaustification for SS via the OI parse: ⟦SS⟧^OI = {wNS, wNSA, wSA} pins down worlds with mixed types.

                                                  NN is vacuously exhaustified: "none" is off-scale, so EXH at any position has no alternatives to exclude. All parses agree.

                                                  AN is maximally informative: all types must satisfy "drank none." Only wN (all N-types) works. All parses agree.

                                                  AA is maximally informative: all types must satisfy "drank all." Only wA works. All parses agree (already proved in aa_singleton).

                                                  NS lit = {wN}: "none drank some" requires no type to satisfy "drank some." S-types and A-types satisfy, so they must be absent. Only wN (all N-types) works.

                                                  SA: "some drank all" requires ≥1 A-type. EXH_I vacuous (inner=all). EXH_O enriches outer "some" to "some but not all," excluding wA (where all types are A → "all" would hold).

                                                  NA: "none drank all" requires no A-types. True at {wN, wNS, wS}. Matrix EXH negates the stronger alternative NS (= "none drank some" = {wN}), removing wN.

                                                  SN: "some drank none" requires ≥1 N-type. True at {wN, wNS, wNA, wNSA}. EXH_O enriches outer "some" to "some but not all," negating AN = {wN}. Matrix EXH gives the same result (M ⊆ O for SN).

                                                  AS: "all drank some" requires every type to satisfy "drank some." EXH_I enriches to "all drank [some but not all]" — only S-types satisfy, so only wS (all S-types) works. Matrix EXH without I negates AA, excluding wA (where "all drank all" holds).

                                                  Literal meaning equals exhaustified meaning under the empty parse.

                                                  ⟦SS⟧^M = {wNS}: matrix EXH narrows SS to a single world. This is the paper's key finding (§6): the M reading "uniquely singles out this world state" (p. e86, eq. 22), giving GI its decisive advantage over LI and LU.

                                                  LI cannot access M-containing parses: none of {lit, I, O, OI} include matrix EXH. This excludes ⟦SS⟧^M from LI's parse space.

                                                  LU cannot access M-containing parses: neither lit nor OI include matrix EXH. This excludes ⟦SS⟧^M from LU's parse space.

                                                  Connection to @cite{potts-etal-2016} #

                                                  The LU model here uses Latent := LULex (2 lexica: lit, OI), the same RSAConfig architecture as @cite{potts-etal-2016}'s Latent := Lexicon (weak, strong "some"). Both implement Bergen et al.'s lexical uncertainty via RSAConfig's latent variable mechanism — no special LUScenario infrastructure needed. Our LU uses L1 only; the paper adds an S2/L2 layer (eqs. 14a-14b) for production predictions.

                                                  Connection to @cite{cremers-wilcox-spector-2023} #

                                                  Cremers et al. test 9 model variants, including EXH-LU and RSA-LI, which correspond to our luConfig and liConfig. Their key finding — grammatical models (EXH-LU, RSA-LI) block anti-exhaustivity regardless of prior bias — is consistent with our LU and LI predictions: both correctly derive exhaustification for SS (lu_ss_prefers_wNS, li_ss_outer_exh), concentrating probability on the exhaustified worlds.

                                                  Connection to @cite{franke-2011} #

                                                  The matrix EXH in exhMeaning uses exh_MW: conjoin the sub-matrix meaning with the negation of all strictly stronger alternatives' literal meanings. @cite{franke-2011} proves that IBR fixed points equal exh_MW for scalar games (Franke2011.r1_eq_exhMW_under_totality). This grounds the matrix position of our GI model in game-theoretic equilibrium semantics.

                                                  Connection to @cite{fox-2007} #

                                                  The exhaustification implementation uses compositional enrichment at inner/outer quantifier positions (scale-mate substitution on {some, all}) and MW-style sentential alternative negation at matrix position. The models differ only in which EXH positions are available to the speaker/listener, demonstrating that the key theoretical question is not HOW to exhaustify but WHERE EXH can be inserted and WHO controls the insertion (grammar vs. speaker vs. listener).

                                                  Why GI wins (§6) #

                                                  The decisive advantage of GI over LI and LU is the M parse: m_ss_singleton proves ⟦SS⟧^M = {wNS}, and li_excludes_matrix / lu_excludes_matrix prove that M-containing parses are structurally unavailable to the smaller models. Only GI can access the reading that uniquely identifies wNS, explaining its superior production predictions for SS (Figure 5, Figure 7c).