Documentation

Linglib.Theories.Pragmatics.NeoGricean.Constraints.NumericalExpressions

OT Constraints for Numerical Expressions #

@cite{cummins-2015} @cite{cummins-franke-2021}

Optimality-Theoretic constraint system for numeral production. Speakers choose among candidate numeral expressions by optimizing across four ranked constraints:

  1. INFO (informativeness): prefer smaller admitted set
  2. Granularity: match contextual precision level
  3. QSIMP (quantifier simplicity): prefer bare numerals
  4. NSAL (numeral salience): prefer round/salient numerals

The key insight connecting to the k-ness model (Phenomena.Gradability.Imprecision.Numerals): NSAL violations = maxRoundnessScore - roundnessScore(n). Rounder numbers incur fewer NSAL violations, making them preferred candidates.

Connection to RSA #

The OT constraints map onto RSA parameters:

Connection to @cite{cummins-franke-2021} #

enrichmentWidth predicts pragmatic enrichment range width from roundnessScore. 100 (score 6) gets a wider enriched range than 110 (score 2), explaining why "more than 100" has weaker argumentative strength per C&F's pragmatic reversal.

Form of the numeral expression.

Bare numerals are simplest; modified forms add complexity.

Instances For
    Equations
    • One or more equations did not get rendered due to their size.
    Instances For

      A candidate numeral expression for OT evaluation.

      • numeral :

        The numeral used

      • actualValue :

        Actual value being communicated

      • Quantifier form

      • contextGranularity :

        Contextual granularity level (trailing zeros in context numeral)

      Instances For
        Equations
        • One or more equations did not get rendered due to their size.
        Instances For

          Infer granularity from a numeral's trailing zeros.

          100 → 2 (precision to hundreds) 110 → 1 (precision to tens) 111 → 0 (precision to units)

          Equations
          • One or more equations did not get rendered due to their size.
          Instances For

            INFO (informativeness): prefer more informative expressions.

            Violations = |admitted set| - 1. An expression that admits more values is less informative and incurs more violations.

            Equations
            Instances For

              Granularity: match the contextual precision level.

              Violations = absolute difference between context granularity and utterance granularity. A granularity mismatch (e.g., saying "100" when context demands unit precision) is penalized.

              Equations
              • One or more equations did not get rendered due to their size.
              Instances For

                NSAL (numeral salience): prefer round/salient numerals.

                Violations = maxRoundnessScore - roundnessScore(n). Maximally round numbers (score 6) incur 0 violations. Non-round numbers (score 0) incur 6 violations.

                This is the key connection to the k-ness model: NSAL is the complement of the graded roundness score.

                Equations
                Instances For

                  An OT constraint with name, violation function, and rank.

                  Higher rank = more dominant in the hierarchy.

                  Instances For
                    Equations
                    • One or more equations did not get rendered due to their size.
                    Instances For

                      The four constraints with default ranking.

                      Equations
                      Instances For

                        Default ranking: INFO >> Granularity >> QSIMP >> NSAL.

                        Equations
                        • One or more equations did not get rendered due to their size.
                        Instances For

                          Violation profile for a candidate: one Nat per constraint, in ranking order.

                          Instances For
                            Equations
                            • One or more equations did not get rendered due to their size.
                            Instances For
                              Equations
                              • One or more equations did not get rendered due to their size.
                              Instances For

                                Compute the violation profile for a candidate.

                                Equations
                                • One or more equations did not get rendered due to their size.
                                Instances For

                                  Lexicographic comparison of paired violation counts. Returns true if the first profile wins at the first point of difference. Factored out for provability (e.g., transitivity of harmonic bounding).

                                  Equations
                                  Instances For

                                    OT strict domination: v1 harmonically bounds v2 if at the first constraint where they differ (in ranking order), v1 has fewer violations.

                                    Equations
                                    • One or more equations did not get rendered due to their size.
                                    Instances For

                                      Select the optimal candidate from a list (first candidate that is not harmonically bounded by any other).

                                      Equations
                                      • One or more equations did not get rendered due to their size.
                                      Instances For

                                        Enrichment width: predicted pragmatic enrichment range from roundnessScore.

                                        Connects to CumminsFranke2021.lean's pragmatic enrichment model:

                                        • 100 (score 6) → wider enrichment (±10, 20 total)
                                        • 110 (score 2) → narrower enrichment (±5, 10 total)
                                        • 7 (score 0) → minimal enrichment (±2, 4 total)

                                        Semantically, "more than 110" is stronger than "more than 100" (higher Bayes factor). But pragmatic enrichment reverses this: "more than 100" enriched to (100,150] retains assertability in goal-worlds, while "more than 110" enriched to (110,120] loses nearly all goal-world assertability. So pragmatically, "more than 100" becomes stronger.

                                        Equations
                                        • One or more equations did not get rendered due to their size.
                                        Instances For

                                          NSAL violations as a normalized RSA cost ∈ [0, 1].

                                          This bridges the OT constraint to the RSA cost parameter: round numerals are "cheap" (cost ≈ 0), non-round are "expensive" (cost ≈ 1).

                                          Equations
                                          Instances For

                                            The four OT constraints as a criterion type.

                                            Instances For
                                              Equations
                                              • One or more equations did not get rendered due to their size.
                                              Instances For

                                                A SatisfactionOrdering over violation profiles using the four @cite{cummins-2015} constraints.

                                                This coarse-grains the OT system: a candidate "satisfies" a constraint iff it incurs 0 violations on that constraint. The resulting ordering is weaker than OT's lexicographic ranking — OT additionally discriminates by violation degree — but captures the structural backbone: a candidate that satisfies a strict superset of constraints is always OT-preferred.

                                                Equations
                                                • One or more equations did not get rendered due to their size.
                                                Instances For

                                                  A candidate with zero violations everywhere is at-least-as-good as any other under the satisfaction ordering.

                                                  theorem NeoGricean.Constraints.NumericalExpressions.zero_bounds_any_violated (v : ViolationProfile) (h : v.info > 0 v.granularity > 0 v.qsimp > 0 v.nsal > 0) :
                                                  harmonicallyBounds { info := 0, granularity := 0, qsimp := 0, nsal := 0 } v = true

                                                  Bridge: zero violations harmonically bounds any profile with at least one violation. This is the strongest case of the general principle that satisfaction-ordering dominance implies OT dominance when the superset constraint is the highest-ranked difference.

                                                  The converse fails: harmonicallyBounds can distinguish candidates that satisfy the same set of constraints but differ in violation degree (e.g., INFO violations 1 vs 3).