@cite{waldon-degen-2021} — Continuous-Incremental RSA (CI-RSA) #
@cite{cohn-gordon-goodman-potts-2019} @cite{degen-etal-2020}
Waldon, B. & Degen, J. (2021). Modeling cross-linguistic production of referring expressions. Proceedings of the Society for Computation in Linguistics (SCiL) 4, 206–215.
The Model #
CI-RSA synthesizes two RSA extensions:
- Incremental RSA (@cite{cohn-gordon-goodman-potts-2019}): Word-by-word production via the chain rule S1(u|r) = ∏ₖ S1(wₖ | [w₁,...,wₖ₋₁], r)
- Continuous semantics (@cite{degen-etal-2020}): Noisy adjective reliability L^C(r, i) = v^i if i true of r, else 1 - v^i
The incremental meaning function averages continuous semantics over grammatical completions of the current prefix:
X^C(c, i, r) = Σ_{u ⊒ c+i} ⟦u⟧^C(r) / |{u : u ⊒ c+i}|
The utterance set is scene-filtered: only utterances Boolean-true of at least one scene member are included (Figure 1).
Formalization #
This builds on RSAConfig's sequential infrastructure (following
@cite{cohn-gordon-goodman-potts-2019}), adding:
- Continuous (ℚ-valued) meaning instead of Boolean extension-counting
rpow-based s1Score with α = 7- Scene-parameterized configs for cross-condition comparisons
The three predictions are trajectory probability comparisons across
different RSAConfig instances (language × scene).
Predictions #
| # | Prediction | Status |
|---|---|---|
| 1 | English color/size asymmetry: SS > CS | rsa_predict |
| 2 | Cross-linguistic: English SS > Spanish SS | rsa_predict |
| 3 | Spanish flip: CS > SS for redundant size | rsa_predict |
| 4 | Overall: English total > Spanish total | rsa_predict |
Connections #
- Noise theory:
lexContinuousQinstantiates the unified noise channel fromRSA.Core.Noise. SeelexContinuous_as_noiseChannel. - Incremental RSA: Extends @cite{cohn-gordon-goodman-potts-2019} with continuous semantics and cross-linguistic word order variation.
Words available to the incremental speaker: two color adjectives, two size adjectives, a noun ("pin"), and an explicit stop token. The stop token models the speaker's choice to end the utterance; without it, postnominal word orders lack a way to represent the stopping decision after the noun (cf. English where "pin" naturally terminates utterances).
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Whether a word is veridically true of a referent.
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- Phenomena.Reference.Studies.WaldonDegen2021.wordApplies Phenomena.Reference.Studies.WaldonDegen2021.Word.blue Phenomena.Reference.Studies.WaldonDegen2021.Referent.bigBlue = true
- Phenomena.Reference.Studies.WaldonDegen2021.wordApplies Phenomena.Reference.Studies.WaldonDegen2021.Word.blue Phenomena.Reference.Studies.WaldonDegen2021.Referent.smallBlue = true
- Phenomena.Reference.Studies.WaldonDegen2021.wordApplies Phenomena.Reference.Studies.WaldonDegen2021.Word.red Phenomena.Reference.Studies.WaldonDegen2021.Referent.bigRed = true
- Phenomena.Reference.Studies.WaldonDegen2021.wordApplies Phenomena.Reference.Studies.WaldonDegen2021.Word.red Phenomena.Reference.Studies.WaldonDegen2021.Referent.smallRed = true
- Phenomena.Reference.Studies.WaldonDegen2021.wordApplies Phenomena.Reference.Studies.WaldonDegen2021.Word.big Phenomena.Reference.Studies.WaldonDegen2021.Referent.bigBlue = true
- Phenomena.Reference.Studies.WaldonDegen2021.wordApplies Phenomena.Reference.Studies.WaldonDegen2021.Word.big Phenomena.Reference.Studies.WaldonDegen2021.Referent.bigRed = true
- Phenomena.Reference.Studies.WaldonDegen2021.wordApplies Phenomena.Reference.Studies.WaldonDegen2021.Word.small Phenomena.Reference.Studies.WaldonDegen2021.Referent.smallBlue = true
- Phenomena.Reference.Studies.WaldonDegen2021.wordApplies Phenomena.Reference.Studies.WaldonDegen2021.Word.small Phenomena.Reference.Studies.WaldonDegen2021.Referent.smallRed = true
- Phenomena.Reference.Studies.WaldonDegen2021.wordApplies Phenomena.Reference.Studies.WaldonDegen2021.Word.pin x✝ = true
- Phenomena.Reference.Studies.WaldonDegen2021.wordApplies Phenomena.Reference.Studies.WaldonDegen2021.Word.stop x✝ = true
- Phenomena.Reference.Studies.WaldonDegen2021.wordApplies x✝¹ x✝ = false
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Semantic reliability values v^i. Color adjectives are more reliable than size adjectives: v^color = 19/20 (0.95), v^size = 4/5 (0.8).
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- Phenomena.Reference.Studies.WaldonDegen2021.semanticValueQ Phenomena.Reference.Studies.WaldonDegen2021.Word.blue = 19 / 20
- Phenomena.Reference.Studies.WaldonDegen2021.semanticValueQ Phenomena.Reference.Studies.WaldonDegen2021.Word.red = 19 / 20
- Phenomena.Reference.Studies.WaldonDegen2021.semanticValueQ Phenomena.Reference.Studies.WaldonDegen2021.Word.big = 4 / 5
- Phenomena.Reference.Studies.WaldonDegen2021.semanticValueQ Phenomena.Reference.Studies.WaldonDegen2021.Word.small = 4 / 5
- Phenomena.Reference.Studies.WaldonDegen2021.semanticValueQ Phenomena.Reference.Studies.WaldonDegen2021.Word.pin = 1
- Phenomena.Reference.Studies.WaldonDegen2021.semanticValueQ Phenomena.Reference.Studies.WaldonDegen2021.Word.stop = 1
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Continuous lexical interpretation L^C(r, i). Returns v^i if true, (1 - v^i) if false.
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Boolean utterance truth: conjunction of word applicability.
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All grammatical English (prenominal) utterances, each terminated
by .stop. In English the noun always comes last before stop,
so "pin" naturally precedes the stopping decision.
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All grammatical Spanish (postnominal) utterances, each terminated
by .stop. The stop token is critical here: after [pin, blue],
the S1 chooses between .stop (2-word non-redundant) and .small
(continuing to the 3-word redundant utterance). Without .stop,
the model forces continuation whenever valid extensions exist.
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Scene-filtered utterances: only those Boolean-true of at least one scene member (Figure 1). This yields 7 utterances per scene.
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Per-word production cost (Section 4): each adjective incurs cost 0.1. Pin and stop have zero cost (noun and utterance boundary).
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Incremental continuous meaning: average continuous semantics over all grammatical completions of prefix.
X^C(c, i, r) = Σ_{u ⊒ c+i} ⟦u⟧^C(r) / |{u : u ⊒ c+i}|
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Real-valued continuous meaning (for RSAConfig).
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- Phenomena.Reference.Studies.WaldonDegen2021.continuousMeaning utts scene pfx r = ↑(Phenomena.Reference.Studies.WaldonDegen2021.continuousMeaningQ utts scene pfx r)
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Size-sufficient scene: {big_blue, big_red, small_blue}. Target small_blue is uniquely identified by size alone.
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- Phenomena.Reference.Studies.WaldonDegen2021.ssScene Phenomena.Reference.Studies.WaldonDegen2021.Referent.bigBlue = true
- Phenomena.Reference.Studies.WaldonDegen2021.ssScene Phenomena.Reference.Studies.WaldonDegen2021.Referent.bigRed = true
- Phenomena.Reference.Studies.WaldonDegen2021.ssScene Phenomena.Reference.Studies.WaldonDegen2021.Referent.smallBlue = true
- Phenomena.Reference.Studies.WaldonDegen2021.ssScene x✝ = false
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Color-sufficient scene: {small_red, big_red, small_blue}. Target small_blue is uniquely identified by color alone.
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- Phenomena.Reference.Studies.WaldonDegen2021.csScene Phenomena.Reference.Studies.WaldonDegen2021.Referent.smallRed = true
- Phenomena.Reference.Studies.WaldonDegen2021.csScene Phenomena.Reference.Studies.WaldonDegen2021.Referent.bigRed = true
- Phenomena.Reference.Studies.WaldonDegen2021.csScene Phenomena.Reference.Studies.WaldonDegen2021.Referent.smallBlue = true
- Phenomena.Reference.Studies.WaldonDegen2021.csScene x✝ = false
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CI-RSA configuration parameterized by utterance set and scene.
- L0 uses extension-based continuous meaning, returning 0 for referents outside the scene
- S1 uses
rpow-based scoring with α = 7 and per-word cost C(i) - S1(i|c,r) ∝ L0(r|c,i)^α · exp(−α · C(i)) (Section 4)
Note: v^color = 0.95 here, matching the paper's fitted values.
This differs from the @cite{degen-etal-2020} value of v^color = 0.99
used in RSA.Core.Noise, because the two papers fit different
experimental datasets.
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English (prenominal) CI-RSA in size-sufficient scene.
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English (prenominal) CI-RSA in color-sufficient scene.
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Spanish (postnominal) CI-RSA in size-sufficient scene.
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Spanish (postnominal) CI-RSA in color-sufficient scene.
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Color adjectives have higher reliability than size adjectives. This asymmetry drives the redundant modification predictions.
All semantic values are positive (required for valid probability).
lexContinuousQ is an instance of the unified noise channel from
RSA.Core.Noise. The continuous lexical semantics L^C(r, i) is
exactly the noise channel with onMatch = v^i, onMismatch = 1 - v^i,
b = 1 if item i is true of referent r, 0 otherwise.
This connects @cite{waldon-degen-2021} to the @cite{degen-etal-2020} parameterization where mismatch = 1 - match.