An Information-Theoretic Explanation of Adjective Ordering Preferences
Michael Hahn1 , Judith Degen1 , Noah Goodman1 , Dan Jurafsky1 , Richard Futrell2{mhahn2, jdegen, ngoodman, jurafsky}@stanford.edu, futrell@mit.edu
1 Stanford University, 2 MIT
Abstract
Across languages, adjectives are subject to ordering restrictions. Recent research shows that these are predicted by adjective subjectivity, but the question remains open why this is
the case. We first conduct a corpus study and not only replicate
the subjectivity effect, but also find a previously undocumented
effect of mutual information between adjectives and nouns.
We then describe a rational model of adjective use in which
listeners explicitly reason about judgments made by different
speakers, formalizing the notion of subjectivity as agreement
between speakers. We show that, once incremental processing is combined with memory limitations, our model predicts
effects both of subjectivity and mutual information. We confirm the adequacy of our model by evaluating it on corpus data,
finding that it correctly predicts ordering in unseen data withan accuracy of 96.2 %. This suggests that adjective ordering
can be explained by general principles of human communication and language processing.

Introduction
Across languages, sequences of modifying adjectives show
preferences for some orderings over others. In English, ‘large
wooden table’ is preferred to ‘wooden large table’, and ‘beautiful green shirt’ is preferred to ‘green beautiful shirt’. Suchpreferences exist across geographically and typologically diverse languages (Dixon, 1982; Sproat & Shih, 1991).
A variety of explanations for these preferences have beenoffered in the literature, including both semantic and syntactic ones. Syntactic accounts assume a rigid syntactic ordering of projections hosting different kinds of adjectives(Scott, 2002; Cinque, 2010). Semantic accounts have appealed to notions such as specificity (Ziff, 1960), inherentness(Whorf, 1945), absoluteness (Sproat & Shih, 1991), conceptformability (Svenonius, 2008), and subjectivity (Hetzron,
1978; Hill, 2012; Scontras, Degen, & Goodman, 2017).
While not all of these hypotheses have been verified ona broader empirical basis, there is strong empirical support
for the idea that adjective subjectivity determines ordering:
Scontras et al. (2017) compared order preferences with ratings of subjectivity for individual adjectives in English, andshowed that subjectivity explained over 60 % of the variancein order preference ratings. They found that more subjective
adjectives tend to occur before less subjective ones.
If these preferences occurred in only a few languages, itwould be reasonable to accept this as an arbitrary fact ofgrammar. But the cross-linguistic stability of the patterns
calls for a general explanation: As they occur in languages
with widely different grammatical structures, we can expectthat such an explanation will make reference to general principles of human communication and cognition. The aim of
this paper is to present such an explanation. We first describe
a corpus analysis, demonstrating effects of both subjectivity

and mutual information on adjective ordering. We then provide an explanatory model of rational adjective use that predicts these effects, and verify that it correctly accounts for the
corpus data.

Corpus Analysis: Subjectivity and Mutual
Information Effects
While previous hypotheses about adjective ordering such as‘specificity’ and ‘inherentness’ of adjectives to nouns (Ziff,
1960; Whorf, 1945)) suggest that adjective ordering should
depend on the noun, Scontras et al. (2017) found no evidence
for noun-specific effects. As their study used selected out-ofcontext noun phrases, one might wonder whether such effects
can be shown using corpus data. As a formalization of specificity, we consider Pointwise Mutual Information:
PMI(Adj, Noun) = log P(Adj|Noun) − log P(Adj)(1)

where P(Adj|Noun) is the probability that the adjective Adj
occurs, given the noun Noun. This concept is a common measure of collocation (Manning & Schuetze, 1999), and measures the degree to which the two words appear together more
frequently than would be expected by chance. If an adjective is specific to a noun, we expect the adjective to appear
more frequently with the noun than with most other nouns,
which is captured by PMI. Following the specificity theory,
our hypothesis is that adjectives with higher mutual information with the noun tend to come closer to the noun. Indeed,words with high mutual information occur closer together inlanguage (Qian & Jaeger, 2012; Gildea & Jaeger, 2015).
Methods and Results We used the BookCorpus (Zhu etal., 2015), a corpus of 11,038 English novels, encompassing
about 74 Million sentences.1 We estimated mutual information between adjectives and nouns from a randomly selected
set of sentences, amounting to about 70 % of the corpus.
The conditional probabilities P(Noun|Adj) are determined bycounting all occurrences where Noun occurred directly after
Adj. However, these counts will be impacted by the existing
adjective ordering preferences, creating a potential confound.
To eliminate this confound, we randomized the order of adjectives occurring in a sequence when counting occurrences.
We then extracted all occurrences of two adjectives between a determiner and a noun from a held-out section
amounting to 10 % of the corpus. We retained those occurrences where both adjectives occurred in the experiment of
1 A reviewer points out that BookCorpus contains duplicate novels, which might result in imprecise MI estimates. Future work
should confirm our results on further datasets.

PMI A1 – N
PMI A2 – N
Subjectivity A1
Subjectivity A2

β

SE

z

p

−0.501
0.501
8.28
−8.28

0.041
0.041
1.35
1.35

−12.2
12.2
6.12
−6.12

< 2.2 · 10−16< 2.2 · 10−16
9.36 · 10−10
9.36 · 10−10

Table 1: Logistic mixed-effects model predicting whether
two given adjectives A1 , A2 were ordered as A1 A2 (coded +1)
or A2 A1 (coded 0), from mutual information and subjectivity.
Scontras et al. (2017), in order to use their experimentallymeasured subjectivities. 4699 datapoints remained.
For each corpus, we ran a logistic mixed-effects model predicting the order of each pair of adjectives, including as fixed
effects (1) subjectivity of the two adjectives from the datacollected by Scontras et al. (2017), (2) mutual information
between the noun and each of the two adjectives. The two
adjectives were entered as random intercepts. The resulting
models are shown in Table 1. We observed main effects ofboth mutual information and subjectivity, such that more objective adjectives and adjectives with higher mutual information with the noun occurred closer to it. Model comparison
with a corresponding model without mutual information predictors (BIC 241, p < 2.2 · 10−16 ) or without subjectivity predictors (BIC 120, p = 2.3 · 10−10 ) confirms that both types ofpredictors contribute independently.

The Function of Subjective Adjectives
Our goal is a formal model of adjective ordering preferencesthat falls out of considerations of communicatively efficientadjective use. One route is by understanding adjectives as
restricting a set of referents, picking out from contextually
given objects denoted by a noun the one that matches the adjective, as done in much of the literature on content selection in referring expressions (Sedivy, Tanenhaus, Chambers,& Carlson, 1999). However, establishing reference is not the
only use of noun phrases, nor do all adjectives simply restricta set of referents. Adjectives often are used non-restrictively,
describing or commenting on some referent. As an example,
consider ‘Forrest looks at the massive crowd.’ (Roth, 1993)– this does not mean that there were two crowds, and Forrest
Gump chose to look at the one that was massive – instead, thesentence means that Forrest looked at the crowd, and the narrator (or Forrest) considered the crowd to be massive. In theliterature, this use is known as non-restrictive use, as opposed
to restrictive use that picks out one of the contextually given
elements matching the noun. Our model will thus be centered not around restriction of reference, but around speakers
communicating descriptions of and attitudes to referents.
Scontras et al. (2017) et al. used two operationalizationsof subjectivity: In their main experiment, they directly asked
participants ‘how subjective’ a given adjective was. They validated this measure by another experiment in which they described two people disagreeing on a judgment, and asking

whether both people could conceivably be right. These measures were highly correlated (r2 = 0.91).
The latter criterion is known as faultless disagreement:
Adjectives are subjective if people can reasonably disagree,
without anyone having to be in error (Kölbel, 2004). In linewith the ‘faultless disagreement’ diagnostic, subjectivity is
typically understood to refer to judgments whose truth is relative to individuals (Kölbel, 2004; Lasersohn, 2005).
It seems, therefore, that the most natural way of modeling
subjective meaning is by explicitly making reference to theopinions of different persons. In our model, we will assume
that listeners infer not just properties of objects, but they infer
and reason about judgments made by different speakers.

A Model of Adjective Use
In this section, we describe a simple formal model of adjective use. Given that subjectivity essentially refers to the potential for disagreement across speakers, we will explicitly
model judgments made by different speakers about objects.
Judgments are objective if speakers tend to agree, while they
are more subjective if speakers are less likely to agree.
We formalize adjectives as expressing judgments A ∈{green, beautiful, ...}, made by a person s about a referent x.
In the case of highly objective adjectives, such as material adjectives, speakers will mostly agree on their judgments, while
they may disagree for more subjective adjectives. The possible states of the worlds are truth-value assignments to the setof expressions{A(s, x) : A an adjective, x a referent, s a person}
where A(s, x) indicates that person s judges referent x to haveproperty A (e.g., green, beautiful, ...). We assume that there
are fixed sets of persons, referents, and properties.
This is illustrated in Figure 1, showing a typical worldstate: Two speakers mostly agree on more objective judgments, such as material and color, and agree less on more
subjective judgments such as size or beauty.
In our model, listeners aim to infer not just judgments of
one speaker, but a full world state including multiple persons.
This is useful when we consider that a listener might laterinteract with other persons. For instance, we expect that alistener learning that one of the persons in Figure 1 judges areferent to be ‘green’ will find it useful to infer that the otherperson likely applies the same judgment – that is, listeners
will generalize objective judgments across people.
World Prior and Inter-Speaker Agreement A world stateis a truth value assignment to all the expressions A(s, x),
across adjectives, persons, and objects. Speakers and listeners share probabilistic prior beliefs about which world states
are more or less likely to be true, formalized by a prior distribution over world states. In our setting, adjectives differ
in the correlation between judgments by different speakers
about the same object. Formally, we assume that for each adjective A, there is a number κ(A) such that, under the prior

METALGREEN
LARGE
BEAUTIFUL

X
X
X

X
X
X
X

METAL
GREEN
LARGE
BEAUTIFUL

X
X
X

in the subscript is true, else 0. In Figure 2, we visualize theincremental updates for the utterance ‘big green tree’.
When choosing which utterance u to utter, speakers trade
off communicative utility U(u) and the cost of production
C(u) using a softmax decision rule:

X
X
X

Figure 1: A typical world state: Speakers are likely to agree
on more objective judgments, and less likely to agree on more
subjective judgments.
over world states, the Pearson correlation between the truth
values of A(s, x) and A(s0 , x) is equal to κ(A), whenever s, s0
are two different speakers. In the special setting where there
are two persons s1 , s2 , this reduces to two Bernoulli variables
with fixed means and correlation, and we can writeA(s1 , x) ∼ Bernoulli(φ)
A(s2 , x) ∼ Bernoulli((1 − κ(A)) · φ + κ(A) · A(s1 , x))(2)

with φ ∈ [0, 1]. The magnitude of κ(A) formalizes correlationof judgments across speakers: Adjectives that show agreement across speakers have κ(A) close to 1. For more subjective adjectives, κ(A) is smaller.
Communication: Rational Listeners and Speakers In
our model, speakers aim to communicate judgments about
objects by uttering three-word phrases consisting of two adjectives and a noun. An utterance A1 A2 N is true for the
speaker in a world if the speaker judges that the adjectives
A1 , A2 both apply to the referent of the noun. That is, thetruth value depends on those parts of the world state that relate to the speaker, but not on those that relate to other persons. In this model, we assume that there is a known mappingfrom nouns to entities, though this assumption can be relaxed.
Our model is couched in the framework of Bayesian modelsof communication (Franke, 2010; Frank & Goodman, 2012;
Goodman & Frank, 2016), consisting of a literal listener anda speaker reasoning about the listener.
We will start with a listener who hears an utterance, and incrementally updates her belief about the world. While incrementality will not be necessary for deriving the core prediction of our model, we want to make explicit how the model
fits with the known psycholinguistic fact that adjectives areprocessed incrementally (Sedivy et al., 1999). When hearing a sequence A1 A2 N, the listener maintains a buffer of thewords heard so far, and conditions her belief by restricting
to those worlds compatible with possible continuations of thebuffer:0
Plistener(w) := Pprior (w)10Plistener(w) ∝ Plistener(w) · δ∃u=A1 A02 N 0 : w|=s u
21
Plistener(w) ∝ Plistener(w) · δ∃u=A1 A2 N 0 : w|=s u

(3)

3
2
Plistener(w) ∝ Plistener(w) · δw|=s A1 A2 N

where w |=s u is a shorthand for ‘the utterance u is true forthe speaker in the world state w’, and δ... is 1 if the condition

Pspeaker (u) ∝ exp(α · (U(u) − β ·C(u)))(4)

Here, α > 0 indicates the degree of rationality, while β > 0
trades off utility and cost. When the speaker has perfectknowledge of the world state, a natural choice for U(u) is
the negative surprisal of the true world state under the posterior belief of the listener (Frank & Goodman, 2012). In
our case, the speaker does not have full information about theworld, as she might not know the judgments made by otherspeakers. Therefore, we take for U(u) the expected negative
posterior surprisal of the ground truth about the other speakers: This quantity is equal (up to a constant independent ofu) to the negative KL divergence between the speaker’s belief
and the listener’s posterior belief after hearing the utterance– a common utility function in rational models of languageuse (Goodman & Stuhlmüller, 2013; Regier, Kemp, & Kay,
2015):
U(u) := − KL(Pspeaker ||Plistener (·|u))= ∑ Pspeaker (w) logw

Plistener (w|u)
Pspeaker (w)(5)

We assume that Pspeaker is equal to the prior conditioned on
the ground truth judgments of the speaker.
For the cost C(u), we take the surprisal of the utteranceu = A1 A2 N according to a general language model – e.g., describing the statistics of a community’s language use.
C(A1 A2 N) = − log P(A1 A2 N)(6)

Unlike the utility function, this cost function is purely aproperty of the surface string A1 A2 N in the statistics of thelanguage, without reference to meaning. We assume thatthe speaker also computes these probabilities incrementally
word-by-word. We assume that the language model encodes
no prior ordering preference – both orderings of an adjective
pair will have the same probability and thus the same cost as
long as this probability P(A1 A2 N) is evaluated exactly.

Adding Noise
So far, while sequences such as ‘beautiful green tree’ and‘green beautiful tree’ result in different sequences of belief
updates in the listener, the final result will be identical. Thus,
both sequences so far have the same communicative utility.
Similarly, in a setting where no prior order preferences are
encoded in the language model, they have the same cost.
We now show how processing and specifically memory
limitations break this symmetry, predicting both subjectivity
and mutual information effects. It has by now been established that, during language production and language comprehension, linguistic material further in the past becomesTimesteps
New Input
BufferCompatible
continuations

T0

big beautiful tree
beautiful green car...
Speaker
Other

T1
big
big
big beautiful tree
big green car...
Speaker
Other

T2
green
big green
big green tree
big green car...
Speaker
Other

T3
tree
big green tree
big green tree

Speaker

Other

BEAUTIFUL
BIG
GREEN

Figure 2: Simulated incremental inference in a listener hearing ‘big green tree’, about judgments made by the speaker andanother person about two objects. The listener maintains a buffer of words received so far, and in each step, considers all
possible continuations (top). The bottom part shows the listener’s incremental posterior belief about the world. For expository
purposes, we assume a simple setting where there are two persons, two objects, and three properties, with κ(beautiful) = 0.3,
κ(big) = 0.5, κ(green) = 0.95. The strength of the color in each cell indicates the listener’s degree of belief that the given
person (column) would judge a given property (row) to apply the given object (column). In each step, the listener considers
all world states that are compatible with potential continuations of the buffer, and accordingly updates her belief about the
speaker’s judgments. To the extent that persons tend to agree about properties, the listener can infer that the other person likely
has the same judgments. This effect is strong for the objective property (‘green’), and weak for the subjective property (‘big’).

Timesteps
New Input
BufferCompatible
continuations

T3
tree??? green tree
beautiful green tree
big green tree
Speaker
Other

BEAUTIFULBIG
GREENTimesteps
New Input
BufferCompatible
continuations

T3
tree??? big tree
beautiful big tree
green big tree
Speaker
Other

BEAUTIFUL
BIG
GREEN

Figure 3: Simulated posterior listener belief if the first adjective is lost when the noun is reached, for input ‘big green tree’(top) and ‘green big tree’ (bottom), in the same setting as Figure 2. If the objective adjective is retained (top), information
generalizes across speakers. Retaining the subjective adjective (bottom) is less useful due to potential for disagreement
between speakers.

harder to access and integrate with new material. Classicalfamilies of examples is include dependency locality effects(Gibson, 1998) and models of cue retrieval in sentence processing (McElree, 2001; Lewis & Vasishth, 2005).
To formally integrate such memory limitations into ourmodel, we follow Futrell and Levy (2017), assuming that during incremental processing, previous words in the input may
be deleted stochastically. Crucially, the probability of a word
being deleted increases as one goes further back in the sequence (Futrell & Levy, 2017).
In our model, there are two places where incremental processing can be affected by noise: the listener’s incremental
belief updates, and the computation of cost.
Noisy Belief Updates First, let us consider what happens
when the listener’s buffer is affected by progressive noise. Let
us consider the simple case where, at each step, at most the
two last words were integrated: The belief updates after hearing the two adjectives are as before. When encountering thenoun, the first adjective is the furthest away from the current
input word, and – in this case – deleted from the buffer.
When computing the posterior, only the last two words areavailable, and the listener considers the possible completionsof the now incomplete buffer (compare Equation 3):
3
2
Pblistener(w) ∝ Plistener(w) · δ∃A01 :w|=s A01 A2 N

(7)

where, as before, w |=s u is a shorthand for ‘the utterance u istrue for the speaker in the world state w’. As noise is stochastic, utility U(u) is now the expected KL-divergence, where
the expectation is taken over the possible noise patterns.
In Figure 3, we illustrate the listener’s state when reaching
the noun, for the two possible orderings of the more subjec-

tive adjective ‘big’ and the less subjective adjective ‘green’.
Depending on which adjective was subject to deletion, the listener has different posterior beliefs not just about the speaker,
but also about the other person: Due to the objective natureof ‘green’, integrating this adjective (top) provides information that generalizes across speakers. As loss is progressive,the first adjective is more likely to be lost when the noun isreached. Thus, placing the objective adjective closer to thenoun is predicted to, on average, result in lower levels of uncertainty about the full state of the world.
Noise in the Cost The second place that involves incremental computation and will thus be affected by progressive noiseis the cost term. If the first adjective is lost when computing
the conditional probability P(N|A1 A2 ) of the noun in context,
the calculation marginalizes out the first adjective and the resulting quantity will be P(N|A2 ). Thus, cost will be estimatedas − log P(A1 )P(A2 |A1 )P(N|A2 ) in this case. Using the definition of PMI, we can writeC(A1 A2 N) −C(A2 A1 N) = λ · (PMI(A1 , N) − PMI(A2 , N))(8)
More generally, Futrell and Levy (2017) show that the estimated surprisal will be biased towards this value when loss isprogressive. Thus, we predict that putting the adjective withhigher PMI with the noun closer to it results in lower cost.

Simulations
We implemented the model in the probabilistic programming
language WebPPL (Goodman & Stuhlmüller, 2014). We constructed contexts with 20 objects, four properties, and two
persons (one speaker and one other person). For simplicity,
we consider the case where only the first adjective is subject
to loss, at loss rate λ ∈ [0, 1]. For inference, we randomly
sample 10,000 worlds from the world prior and compute thelistener model by exact enumeration of these samples.
We have described how the model predicts subjectivity andmutual information effects, but one might wonder how robust these effects are to changes in parameter values. We
considered the predictions the model makes for different values of the inter-speaker correlations κ(A1 ), ..., κ(Anad j ), theloss probability λ, the rationality parameters α, β > 0, andthe prior probability φ in (2). We sampled α ∼ Γ(5, 1),β ∼ Γ(5, 1), λ ∼ Uniform(0, 1), φ ∼ σ(N (0, 0.5)).
We first considered the setup where A1 is more subjective
than A2 – that is, κ(A1 ) < κ(A2 ), while taking PMI(A1 , N) =PMI(A2 , N). The correlations for other adjectives are uniformly random. We sampled 10,000 parameter settings subject to this constraint. For every single setting, we foundU(A1 A2 N) > U(A2 A1 N) – placing adjectives with higher
inter-speaker correlation closer to the noun increased utility. In Figure 4, we plot utility difference as a function ofκ(A1 ) − κ(A2 ). Utility difference is directly proportional tothe difference in inter-speaker correlations.
We then carried out the same with κ(A1 ) = κ(A2 ) andPMI(A1 , N) < PMI(A2 , N) – that is, assuming that A2 is more

Figure 4: Left: Utility Difference between Orderings, as afunction of the difference between inter-speaker correlations
κ(A1 ), κ(A2 ). Across parameter settings, putting the subjective adjective earlier results in higher utility. Right: Cost
difference, as a function of PMI difference. Placing the adjective with higher PMI closer to the noun results in lower
cost. Both plots show LOESS-smoothed means.
predictive of the noun. In this case, as shown in Equation 8,
difference in cost is proportional to difference in PMI (Figure 4). Thus, assuming noisy memory, both subjectivity andMI are predicted to affect order preferences of the speaker.

Testing against Corpus Data
We tested the speaker model against the data from our corpus
analysis described above. For the inter-speaker correlations
κ(A), we took κ(A) to be one minus the average subjectivity
score from Scontras et al. (2017). For the cost term, we usedmutual information data from the corpus analysis.
We used Bayesian data analysis to infer the numericalparameters of our model (rationality parameters α, β, loss
rate λ, prior probabilty φ) from the BookCorpus data weused in the beginning. We specified priors α · λ ∼ N (0, 10),α · β · λ ∼ N (0, 10), φ ∼ σ(N (0, 2)), where σ is the inverselogit function.2 To obtain approximate posterior distributions, we used variational inference with minibatches in Pyro
(http://pyro.ai/). We obtained posterior means α · λ =5.07 (σ2 = 0.243), α·β·λ = 0.39 (σ2 = 0.033), and φ = 0.095(logit(φ) = −2.253, σ2 = 0.13). The fitted values suggestthat utility is weighted much more strongly than cost, andthat most judgments are relatively unlikely a priori.
Plugging in the posterior means for these parameters, themodel achieves a classification accuracy of 93.7 % on the taskof predicting adjective order on the dataset.3 A model with
only the cost term would achieve an accuracy of 69 %, whilea model with only the utility term achieves an accuracy of
93.3 %, very close to the accuracy of the full model.4 Thishighlights the central role of the utility term – and thus sub2 Our model does not make it possible to obtain independent estimates of α, β and λ.
3 Logistic regression models with surprisal and PMI predictors
would achieve the same accuracy. However, note that our model isan explanatory cognitive model, as opposed to a data analysis.
4 While the cost term does not contribute much in terms of accuracy, a mixed-effects analysis analogous to the corpus analysis above
confirms that it contributes significantly (p < 2.2 · 10−16 ).

jectivity – for ordering. To test whether results generalize tounseen data, we used a further held-out 20 % of the corpus.
Classification accuracy was 93.1 %.
As the prior probability that an adjective is applied toobjects might not be uniform, we also considered the setting where φ in (2) varies with the adjective. We assumed
a hierarchical model with hyperparameters φ0 ∼ N (0, 2),S2 ∼ N (0, 1), and parameters φ(A) ∼ σ(N (φ0 , S2 )) for eachadjective A. We obtained similar estimates: α · λ = 5.6(σ2 = 0.25),α·β = 0.36 (σ2 = 0.088), φ0 = −2.1 (σ2 = 0.12),
S2 = 0.31 (σ2 = 0.069). Classification accuracy increases to97.3 % on the original dataset, and to 96.2 % on the heldout set, which shows that the improvement obtained from theincrease in model complexity generalizes to unseen data. Future research should test the prediction that the inferred values for φ(A) correspond to the prior probability that a speaker
would apply a given adjective to an object (Equation 2).

Discussion
We provided an explanatory cognitive model of adjective ordering, building on the insight that subjectivity and specificity, formalized by mutual information, impact adjective ordering. We first conducted a corpus study and showed that ordering is impacted independently by subjectivity and mutual
information. We then presented a model of adjective use inwhich listeners infer judgments made by speakers and otherpersons. We integrated this model with a recent model ofmemory limitations in language processing, and showed thatit predicts both subjectivity and mutual information effects.
We evaluated the model on corpus data, finding that it predicts adjective ordering with an accuracy of 96.2 %. In thefollowing, we discuss some of the implications of this work,and questions that it raises.
Research has shown that subjective material more generally tends to appear at the periphery of phrases and clauses,
and that diachronic meaning change towards more subjective meanings correlates with movement to the periphery(Traugott, 2010). This is in line with our proposal: Our analysis should equally apply to other types of subjective material,
predicting that memory limitations favor placing them further
away from the head. Future research should test our model onother types of subjective content.
We have assumed that the speaker’s communicative goalis communicating descriptions and attitudes, rather than establishing reference. This was motivated by the observationthat adjectives are often not used for establishing reference.
Future research should compare our account with accountsof adjective ordering preferences that rely on the assumptionthat adjectives are used primarily for reference resolution.
In languages where adjectives follow the noun, suchas Spanish or Arabic, typically the reverse order is observed (Dixon, 1982). Our account seems to make the correct prediction: In such languages, the noun is more likely
to be lost when the second (subjective, in this case) adjective
is reached. We furthermore make the prediction that, in suchlanguages, adjectives with higher mutual information with thenoun will also be more likely to come closer to the noun.
Recently, Dye, Milin, Futrell, and Ramscar (2017) interpreted prenominal modifiers as smoothing entropy, making
nouns more equally predictable, and speculated that this may
account for adjective ordering preferences. A notable difference between this theory and ours is that theirs predicts major differences between ordering patterns of prenominal andpostnominal adjectives, whereas ours is symmetric.
In conclusion, the work reported here suggests that adjective ordering preferences are plausibly the result of efficiently
trading off cost and informational utility of utterances for the
purpose of communicating maximally generalizable information about objects.

References
Cinque, G. (2010). The syntax of adjectives. MIT Press.
Dixon, R. (1982). Where have all the adjectives gone? And other essays in semantics
and syntax. Berlin: Mouton.
Dye, M., Milin, P., Futrell, R., & Ramscar, M. (2017). Cute Little Puppies and Nice
Cold Beers: An Information Theoretic Analysis of Prenominal Adjectives. In 39th
Annual Meeting of the Cognitive Science Society, London, UK.
Frank, M. C., & Goodman, N. D. (2012). Predicting Pragmatic Reasoning in Language
Games. Science, 336, 998–998.
Franke, M. (2010). Signal to Act. Unpublished doctoral dissertation.
Futrell, R., & Levy, R. (2017). Noisy-context surprisal as a human sentence processing
cost model. In Proceedings of EACL.
Gibson, E. (1998). Linguistic complexity: Locality of syntactic dependencies. Cognition, 68(1), 1–76.
Gildea, D., & Jaeger, T. F. (2015). Human languages order information efficiently.
arXiv:1510.02823 [cs]. (arXiv: 1510.02823)
Goodman, N. D., & Frank, M. C. (2016). Pragmatic language interpretation as probabilistic inference. Trends in Cognitive Sciences, 20(11), 818–829.
Goodman, N. D., & Stuhlmüller, A. (2013). Knowledge and Implicature: Modeling
Language Understanding as Social Cognition. Topics in Cognitive Science, 5, 173–
184.
Goodman, N. D., & Stuhlmüller, A. (2014). The Design and Implementation of Probabilistic Programming Languages.
Hetzron, R. (1978). On the relative order of adjectives. In Language Universals (pp.
165–184). Tübingen.
Hill, F. (2012). Beauty Before Age? Applying Subjectivity to Automatic English Adjective Ordering. In Proceedings of the NAACL HLT 2012 Student Research Workshop(pp. 11–16).
Kölbel, M. (2004). Faultless Disagreement. Proceedings of the Aristotelian Society,
104, 53–73.
Lasersohn, P. (2005). Context Dependence, Disagreement, and Predicates of Personal
Taste. Linguistics and Philosophy, 28(6), 643–686.
Lewis, R. L., & Vasishth, S. (2005). An activation-based model of sentence processing
as skilled memory retrieval. Cognitive Science, 29(3), 375–419.
Manning, C., & Schuetze, H. (1999). Foundations of Statistical Natural Language
Processing. MIT Press.
McElree, B. (2001). Working Memory and Focal Attention. Journal of experimental
psychology. Learning, memory, and cognition, 27(3), 817–835.
Qian, T., & Jaeger, T. F. (2012). Cue Effectiveness in Communicatively Efficient
Discourse Production. Cognitive Science, 36(7), 1312–1336.
Regier, T., Kemp, C., & Kay, P. (2015). Word Meanings across Languages Support
Efficient Communication. The handbook of language emergence, 87, 237.
Roth, E. (1993). Forrest Gump [screenplay].
Scontras, G., Degen, J., & Goodman, N. D. (2017). Subjectivity Predicts Adjective
Ordering Preferences. Open Mind, 1, 53–66.
Scott, G.-J. (2002). Stacked adjectival modification and the structure of nominal
phrases. In G. Cinque (Ed.), The cartography of syntactic structures (pp. 91–120).
Oxford.
Sedivy, J. C., Tanenhaus, M. K., Chambers, C. G., & Carlson, G. N. (1999). Achieving
incremental semantic interpretation through contextual representation. Cognition,
71(2), 109–147.
Sproat, R., & Shih, C. (1991). The Cross-Linguistic Distribution of Adjective Ordering
Restrictions. In C. Georgopoulos & R. Ishihara (Eds.), Interdisciplinary Approaches
to Language.
Svenonius, P. (2008). The position of adjectives and other phrasal modifiers in thedecomposition of DP. In L. McNally & C. Kennedy (Eds.), Adjectives and Adverbs:
Syntax, Semantics, and Discourse (pp. 16–42). Oxford: Oxford University Press.
Traugott, E. C. (2010). Revisiting subjectification and intersubjectification. In
K. Davidse, L. Vandelanotte, & H. Cuyckens (Eds.), Subjectification, intersubjectification and grammaticalization (pp. 29–71). Berlin: De Gruyter Mouton.
Whorf, B. L. (1945). Grammatical categories. Language, 21, 1–11.
Zhu, Y., Kiros, R., Zemel, R., Salakhutdinov, R., Urtasun, R., Torralba, A., & Fidler,
S. (2015). Aligning Books and Movies: Towards Story-like Visual Explanations by
Watching Movies and Reading Books. arXiv:1506.06724 [cs]. (arXiv: 1506.06724)
Ziff, P. (1960). Semantic Analysis. Ithaca, NY.