Formalizing the Pragmatics of Metaphor Understanding
Justine T. Kao1 (justinek@stanford.edu), Leon Bergen2 (bergen@mit.edu), Noah D. Goodman1 (ngoodman@stanford.edu)
1 Department of Psychology, Stanford University. 2 Department of Brain and Cognitive Science, MIT.

Abstract
While the ubiquity and importance of nonliteral language areclear, people’s ability to use and understand it remains a mystery. Metaphor in particular has been studied extensively
across many disciplines in cognitive science. One approach focuses on the pragmatic principles that listeners utilize to infer
meaning from metaphorical utterances. While this approachhas generated a number of insights about how people understand metaphor, to our knowledge there is no formal modelshowing that effects in metaphor understanding can arise frombasic principles of communication. Building upon recent advances in formal models of pragmatics, we describe a computational model that uses pragmatic reasoning to interpret
metaphorical utterances. We conduct behavioral experiments
to evaluate the model’s performance and show that our model
produces metaphorical interpretations that closely fit behavioral data. We discuss implications of the model for metaphor
understanding, principles of communication, and formal models of language understanding.
Keywords: language understanding; metaphor; pragmatics;
computational models

Introduction
From “Juliet is the sun” to “That woman is a bombshell,” nonliteral language is, quite literally, everywhere. Metaphor, hyperbole, and sarcasm are ubiquitous features of human communication, often creating poetic or humorous effects that
add rich dimensions to language (Glucksberg, 2001; Pilkington, 2000; Lakoff & Turner, 2009; Roberts & Kreuz, 1994).
Metaphor has inspired a particularly large amount of researchin cognitive science, spanning topics such as how metaphors
structure and shape our thoughts (Ortony, 1993; Lakoff etal., 1993; Thibodeau & Boroditsky, 2011), whether metaphor
processing recruits the same strategies as standard language
processing (Giora, 1997; Gibbs, 2002; Glucksberg & Keysar,
1993) and what factors determine people’s interpretation anovel metaphor (Gentner & Wolff, 1997; Blasko & Connine,
1993; Tourangeau & Sternberg, 1981; Kintsch & Bowles,
2002). This overwhelming interest in metaphor research isdue to both the ubiquity of metaphor in everyday language
and the potential role of metaphor for helping us understandhow the mind creates meaning.
One approach to studying metaphor focuses on the pragmatic principles that listeners utilize to infer meaning from
metaphorical utterances (Tendahl & Gibbs, 2008). Rather
than view metaphor as a separate mode of communicationthat requires specialized language processing strategies, thisapproach argues that basic principles of communication drive
the meaning that a listener infers from a metaphor (Sperber& Wilson, 2008; Wilson & Carston, 2006). Relevance theory, in particular, posits that listeners interpret utterances withthe assumption that speakers produced them because they are
maximally relevant (Wilson & Sperber, 2002). Relevancetheorists argue that this principle explains how listeners infer the meaning of a novel metaphor as well as other forms ofloose talk where the meaning of an utterance is underspecified(Sperber & Wilson, 1985). When interpreting the metaphor“My lawyer is a shark,” for example, the listener assumes thatthe speaker aims to communicate features of “a shark” thatare relevant to the person under discussion (“my lawyer”) anddo not make use of other shark features—vicious but not has
fins or swims.
While many linguists and psychologists have argued forthe benefits of studying metaphor using a pragmatics framework, to our knowledge there is no formal model showingthat effects in metaphor understanding may arise from basic
principles of communication. On the other hand, a recent
body of work presents a series of computational models forpragmatic reasoning, where speaker and listener reason about
each other to communicate effectively (Frank & Goodman,
2012; Jäger & Ebert, 2009). By formalizing principles ofcommunication, these models are able to make quantitative
predictions about a range of phenomena in language understanding, such as scalar implicature and the effect of alternative utterances (Goodman & Stuhlmüller, 2013; Bergen,
Goodman, & Levy, 2012). However, a limitation of thesemodels is that they are unable to explain the use of utterances
which are known to have false literal interpretations. More recent work extends these models to consider affective goals incommunication that may be optimally satisfied by nonliteralutterances such as hyperbole (under review). In this paper, we
will be considering the role of communicative goals in the interpretation of metaphorical utterances. In our formalization,a listener assumes that the speaker chooses an utterance tomaximize informativeness about a subject along dimensions
that are relevant to the conversation and consistent with the
speaker’s communicative goal. This makes it possible for aliterally false utterance to be optimal as long as it is informative along the target dimension. Our framework closely aligns
with the relevance-theoretic view that a listener considers therelevance of a potential meaning to the speaker’s goal in order
to infer what the speaker intended to communicate.
To reasonably limit the scope of our work, we focus on
metaphors of the classic form “X is a Y .” We describe a computational model that can interpret such sentences metaphorically and conduct behavioral experiments to evaluate themodel’s performance. We show that peoples’ interpretationsof metaphors are driven by conversational context and that
our model captures this effect. Finally, we show that ourmodel predictions correlate significantly with people’s finegrained interpretations of metaphorical utterances.

Computational ModelIn the basic Rational Speech Act model of (Frank & Goodman, 2012; Goodman & Stuhlmüller, 2013), a listener and a
speaker recursively reason about each other to arrive at pragmatically enriched meanings. Given an intended meaning,a speaker reasons about a literal listener and chooses an utterance based on its informativeness. A pragmatic listenerthen reasons about the speaker and uses Bayes’ rule to infer
the meaning given the utterance. To account for nonliteralinterpretation, we extend this model by considering the ideathat a speaker may have a range of different communicative
goals. Intuitively, an utterance is optimally informative andrelevant if it satisfies the speaker’s communicative goal. Since
the speaker’s precise communicative goal may be unknown to
the listener, the listener performs joint inference on the goalas well as the intended meaning. By introducing multiple
potential goals for communication, we open up the possibility for a speaker to produce an utterance that is literally false
but still satisfies her goal. The speaker achieves this in partby exploiting her own and the listener’s prior knowledge—
their common ground (Clark, 1996)—to reason about what
information the listener would gain if he takes the utterance
literally.
To illustrate this idea more concretely and demonstrate
how it is implemented in our model, we will use the metaphor“John is a shark” as an example. For simplicity, in this model
we restrict the number of possible categories to which a member may belong to ca and c p , denoting an animal category ora person category, respectively. We also restrict the possible features of John under consideration to a vector of size
three: ~f = [ f1 , f2 , f3 ], where fi is either 0 or 1 (for example,
the three features could be scary, sleek, and finned).1 The literal listener L0 will interpret the utterance “John is a shark”
as meaning that John is literally a member of the category“shark” and has corresponding features. Formally, if u is theuttered category:P(~f |c)
if c = u~
L0 (c, f |u) =0otherwise
where P(~f |c) is the prior probability that a member of category c (in this case “shark” or “person”) has feature vector~f .
We assume that the speaker’s goal is to communicate thevalue of a particular feature—a goal is thus a projection fromthe full feature space to the subset of interest to the speaker.
Formally, the goal to communicate about feature i ∈ {1, 2, 3}
is the function gi (~f ) = fi . Following the Rational Speech
Act model, we define the speaker’s utility as the negative surprisal of the true state under the listener’s distribution, given
an utterance. However, here we consider only the surprisal
along the goal dimension. To do so we project along the goal
dimension, which leads to the following utility function for
1 In principle the model can be extended to accommodate morecategories and features.

speaker S1 :U(u|g, ~f ) = log ∑ δg(~f )=g(~f 0 ) L0 (c, ~f 0 |u)(1)c,~f 0Given this utility function, the speaker chooses an utterance
according to a softmax decision rule that describes an approximately rational planner (Sutton & Barto, 1998):~
S1 (u|g, ~f ) ∝ eλU(u|g, f ) ,(2)

where λ is an optimality parameter.
Imagine that S1 had the goal to convey f1 , scariness, about
John. Based on S1 ’s understanding of L0 ’s prior knowledge,
she knows that if she produces the utterance “John is a shark,”
L0 will believe that John is literally a shark and hence very
likely to be scary. Since S1 ’s goal is satisfied if the listener believes that John is scary, S1 is motivated to produce
such a metaphorical utterance. A pragmatic listener, however, should be able to leverage this pattern to infer that Johnis scary without inferring that John is actually a shark.
The listener L1 performs Bayesian inference to guess theintended meaning given prior knowledge and his internal
model of the speaker. To determine the speaker’s intended
meaning, Ln will marginalize over the possible speaker goals
under consideration:
L1 (c, ~f |u) ∝ P(c)P(~f |c) ∑ P(g)S1 (u|g, ~f )gWhile speaker and listener could continue to reason about
each other recursively, resulting in Ln , we restrict ourselvesto L1 for present purposes. Past work has shown that this firstlevel of pragmatic reasoning is often a good model of humancomprehension.2 If listener L1 thinks it is likely that speaker
S1 ’s goal is to convey scariness but believes it is a priori very
unlikely that John is actually a shark, she will determine that
S1 is using shark metaphorically—that John is a scary person.
Based on this formulation, the listener needs to consider
the following prior probabilities to arrive at an interpretation:(1) P(c): the prior probability that the entity discussed belongings to category c. We assume that the listener is extremely
confident that the person under discussion (e.g. John) is aperson, but that there is a non-zero probability that John isactually a non-human animal. We fit P(ca ) to data with theassumption that 10−4 ≤ P(ca ) ≤ 10−1 .
(2) P(~f |c): the prior probability that a member of categoryc has feature values ~f . This is empirically estimated inExperiment 1.
(3) P(g): the probability a speaker has goal g. This prior canchange based on the conversational context that a question
2 The current model does not robustly predict metaphorical in-

terpretations at recursion depths greater than 1. Future work will
investigate which features of the model lead to this prediction, andwhether this remains true under alternative model definitions.

Animalf1 = 1
f2 = 1 f3 = 1
f1 = 0f2 = 0f3 = 0
Animal f1 = 1
f2 = 1
f3 = 1
f1 = 0f2 = 0f3 = 0ant
small
strong
busy
large
weak
idle
goose
loud
mean annoying
quiet
nice
agreeablebatscaryblind nocturnal unalarming sighted
diurnalhorse
faststrong beautiful
slow
weak
ugly
bear
scary
big
fierce unalarming
small
nonviolent kangaroo jumpy
bouncycute
relaxedinelastic unattractive
bee
busy
small
angry
idle
large
unangry
lion ferocious scarystrong nonviolent unalarming
weak
bird
free
graceful small
unfreeawkwardlarge
monkey funny
smart
playful humorlessstupidunplayfulbuffalobig
strong
wild
small
weak
tame
owl
wise
quiet nocturnal foolish
louddiurnalcat independent lazysoft
dependent
fast
hard
ox
strong
big
slow
weak
small
fast
cow
fat
dumb
lazy
thin
smart
fast
penguin
cold
cute
funny
hot
unattractive humorless
dog
loyal
friendly happy
disloyal unfriendly
unhappy
pig
dirty
fat
smellyclean
thin
fragrant
dolphinsmart
friendly playful
stupidunfriendly unplayfulrabbit
fast
furry
cute
slow
hairless unattractive
duck
loud
cute quacking
quiet
unattractive non-quacking shark
scary dangerous mean unalarming
safe
nice
elephant
huge
smart
heavy
small
stupid
light
sheep
wooly
fluffydumbhairless
hard
smart
fish
scaly
wet
smellysmoothdryfragrant
tiger
stripedfiercescary unpatterned nonviolent unalarming
fox
sly
smart
prettyartless
stupid
ugly
whale
large
graceful majestic
small
awkwardinferior
frog
slimy
noisy
jumpy nonslipperyquiet
relaxed
wolf
scary
mean
angry unalarming
niceunangrygoat
funny
hungryloudhumorless
full
quiet
zebra
stripedexoticfast
unpatterned native
slow

Table 1: 32 animal categories, feature adjectives, and their antonyms. Feature adjectives were elicited from Experiment 1a andindicate when a feature is present ( fi = 1). Antonyms were generated using WordNet and indicate when a feature is not present( fi = 0). Feature sets shown in Experiment 1b were created with this table, where ~f = [1, 0, 0] for category “ant” is represented
by the words {small, weak, idle}. There are 23 = 8 possible feature combinations for each animal category.
sets up. For example, if the speaker is responding to avague question about John, e.g. “What is John like?”, theprior over goals is uniform. If the question targets a specificfeatures, such as “Is John scary?”, then she is much more
likely to have the goal of communicating John’s scariness.
However, she may still want to communicate other features about John that were not asked about. We assume
that when the question is specific, the prior probability that
Sn ’s goal is to answer the specific question is greater than0.5, fitting the value to data below.

Behavioral Experiments
To evaluate our model’s interpretation of metaphorical utterances, we focused on a set of 32 metaphors comparing humanmales to different non-human animals. We conducted Experiment 1a and 1b to elicit feature probabilities for the categories
of interest. We then conducted Experiment 2 to measure people’s interpretations of the set of metaphors.

Experiment 1a: Feature Elicitation
Materials We selected 32 common non-human animal categories from an online resource for learning English (www.englishclub.com). The full list is shown in Table 1.
Methods 100 native English speakers with IP addresses in
the United States were recruited on Amazon’s Mechanical
Turk. Each participant read 32 animal category names presented in random order, e.g. “whale”, “ant”, “sheep”. For
each animal category, participants were asked to type the firstadjective that came to mind in a text box.
Results Using participants’ responses, we constructed a listof adjectives for each animal category and ordered them by
the number of times they were given by a different subject(i.e. their popularity). We removed all color adjectives, suchas “white” and “black,” to eliminate the possibility of interpreting these adjectives as racial descriptions. To avoid redundancy in the feature set, we used WordNet (Miller, 1995)
to identify synonymous adjectives and only kept the mostpopular adjective among a set of synonyms. We then took thethree most popular adjectives for each animal category andused them as the set of features. In what follows, f1 is themost popular adjective, f2 the second, and f3 the third. Table
1 shows the animal categories and their respective features.

Experiment 1b: Feature Prior Elicitation
Materials Using the features collected from Experiment1a, we elicit the prior probability of a feature vector given
an animal or person category (i.e. P(~f |c)). We assume thatthe adjective corresponding to a feature (e.g. scary) indicatesthat the value of that feature is 1 (present), while the adjective’s antonym indicates that the value of that feature is 0 (notpresent). We used WordNet to construct antonyms for each of
the adjective features produced in Experiment 1a. When multiple antonyms existed or when no antonym could be foundon WordNet, the first author used her judgment to choose
the appropriate antonym. Table 1 shows the resulting list ofantonyms. For each animal category, eight possible feature
combinations were constructed from the three features andtheir antonyms. For example, the possible feature combinations for a member of the category “ant” are {small, strong,busy}, {small, strong, idle}, {small, weak, busy}, and so on.
Methods 60 native English speakers with IP addresses in
the United States were recruited on Amazon’s Mechanical
Turk. Each participant completed 16 trials in random order. Each trial consisted of the eight feature combinations
for a particular animal category. Using slider bars with ends
marked by “Impossible” and “Absolutely certain,” participants were asked to rate how likely it is for a member of theanimal category to have each of the eight feature combinations. Participants also rated the probabilities of the feature
combinations for a male person. We only elicited priors formales to minimize gender variation and to maintain consistency with Experiment 2.
Results We normalized each participant’s ratings for theeight feature combinations in a trial to sum up to 1 basedLiteral utterance

Metaphorical utterance

1.00

Probability of feature given utterance

on the assumption that the feature combinations exhaustively describe a member of a particular category. Using
the Spearman-Brown prediction equation, reliability of theratings was 0.941 (95% CI = [0.9408, 0.9414]). Averaging across participants’ normalized ratings, we obtained feature priors P(~f |c) for c = ca (animal) and c = c p (person).
Since the features were created using the animal categories
in Experiment 1a, by construction features are rated as significantly more likely to be present in the animal category
than in the person category (F(1, 190) = 207.1, p < 0.0001).
These results confirm that participants are fairly confident
that each animal category has certain distinguishing features(mean= 0.61, sd= 0.06), while those same features are rated
as appearing in people less often (mean= 0.48, sd= 0.06).

Feature
f1
f2

0.75
f3

0.50

0.25

0.00
Vague goalSpecific goal

Vague goalSpecific goal

Experiment 2: Metaphor Understanding
Materials We created 32 scenarios based on the animal categories and results from Experiment 1. In each scenario, aperson (e.g. Bob) is having a conversation with his friend
about a person that he recently met. Since we are interested in how the communicative goals set up by context affect metaphor interpretation as well as the effectiveness of
metaphorical versus literal utterances, we created four conditions for each scenario by crossing vague/specific goals
and literal/metaphorical utterances. In vague goal conditions,Bob’s friend asks a vague question about the person Bob recently met: “What is he like?” In specific goal conditions,Bob’s friend targets f1 and asks a specific question about theperson: “Is he f1 ?,” where f1 is the most popular adjective
for a given animal category ca . In literal conditions, Bob
replies with a literal utterance, either by saying “He is f1 .”to the question “What is he like?” or “Yes.” to the question“Is he f1 ?”. In Metaphorical conditions, Bob replies with ametaphorical statement, e.g. “He is a ca .” where ca is an animal category. See Table 2 for examples of each condition.
Goal

Utterance

Example question

Example utterance

Vague
Specific
Vague
Specific

Literal
LiteralMetaphorical
Metaphorical“What is he like?”“Is he scary?”“What is he like?”“Is he scary?”“He is scary.”“Yes.”“He is a shark.”“He is a shark.”

Table 2: Example scenarios given the four experimental conditions in Experiment 2.
Methods 49 native English speakers with IP addresses in
the United States were recruited on Amazon’s Mechanical
Turk. Each participant completed 32 trials in random order.
The 32 trials were randomly and evenly assigned to one of thefour conditions, i.e. each participant read 8 scenarios for eachcondition. For each trial, participants used sliders to indicate
the probabilities that the person described has features f1 , f2 ,and f3 , respectively.
Results For each condition of each scenario, we obtained
the average probability ratings for the three features. Fig-

Figure 1: Average probability ratings for the three features
given a vague/specific goal and a literal/metaphorical utterance. Error bars are standard error over the 32 items.
ure 1 shows the average ratings for each feature across animal categories given a vague or specific goal and a literal or metaphorical utterance. When the speaker gives aliteral statement directly affirming the presence of f1 , participants rate f1 as significantly more likely than when the
speaker gives a metaphorical statement (F(1, 126) = 52.6,
p < 0.00001). However, participants rate f2 and f3 as significantly more likely when the speaker produces a metaphoricalutterance than when the utterance is literal (F(1, 126) = 23.7,
p < 0.0001; F(1, 126) = 13.66, p < 0.0005). Comparing feature probability ratings in Experiment 2 to the feature priors
obtained in Experiment 1b, we can measure how literal andmetaphorical utterances change listeners’ inferences about aperson’s features. Given a literal utterance that directly confirms the existence of f1 , probability ratings for f1 are significantly higher than the prior probabilities of f1 for a person(t(63) = 59.19, p < 0.00001). However, probability ratings
for f2 and f3 are not significantly different from their priorprobabilities (t(63) = −0.13, p = 0.89; t(63) = 0.03, p =0.97). Given a metaphorical utterance, probability ratings for
all three features are significantly higher than the prior probabilities (t(63) = 15.74, p < 0.0001; t(63) = 7.29, p < 0.0001;
t(63) = 5.91, p < 0.0001). While preliminary, this analysis suggests that metaphorical utterances may convey richer
information and update listeners’ beliefs along more dimensions than literal utterances.
We now analyze the effect of the speaker’s communicative goal on the interpretation of literal or metaphorical utterances. When the speaker’s utterance is literal, the probability ratings for f1 , f2 , and f3 are not significantly different
given a vague or a specific question ((F(1, 62) = 2.73, p =0.1; F(1, 62) = 0.0001, p = 0.99; F(1, 62) < 0.0001, p =0.99). For metaphorical utterances, however, the question
type has an effect on participants’ interpretations: partici-

Goal

0.8

Vague
SpecificHumanpants rate the probability of f1 as significantly higher whenthe question is specifically about f1 than when it is vague(F(1, 62) = 10.16, p < 0.005). The probabilities of f2 andf3 are not significantly different given a vague question ora specific question about f1 (F(1, 62) = 0.04, p > 0.05;
F(1, 62) = 0.8285, p > 0.05). This suggests that people’sinterpretation of metaphor may be more sensitive to the communicative goals set up by context than their interpretation ofliteral utterances.

0.6

Feature
f1
f2

0.4

f3

Model Evaluation
We used the feature priors obtained in Experiment 1b to compute model interpretations of the 32 metaphors. As discussed
in the previous section, the behavioral results in Experiment
2 show evidence that the context set up by a question changes
participants’ interpretation of a metaphor. Our model naturally accounts for this using the speaker’s prior over communicative goals P(g). When a speaker is responding to avague question, we set the prior distribution for P(g) as uniform. When the speaker is responding to a question specifically about f1 , we assume that P(g1 ) > 0.5 and equal between
P(g2 ) = P(g3 ). Fitting the goal prior parameter to data yields
a prior of P(g1 ) = 0.6 when responding to a specific question about f1 . We fit the category prior P(ca ) = 0.01 and the
speaker optimality parameter λ = 3.
Using these parameters, we obtained interpretation probabilities for each of the 32 metaphors under both vague
and specific goal conditions. For each metaphor and goal
condition, the model produces a joint posterior distribution
P(c, ~f |u). We first show a basic but important qualitative result, which is that the model is able to interpret utterances
metaphorically. Marginalized over values of ~f , the probability of the person category given the utterance is close toone (P(c p |u) = 0.994), indicating that the pragmatic listener
successfully infers that the person described as an animal isactually a person and not an animal. This shows that themodel is able to combine prior knowledge and reason about
the speaker’s communicative goal to arrive at nonliteral interpretations of utterances.
We now turn to the second component of the interpretation,
P(~f |u). To quantitatively evaluate the model’s performance,
we correlated model predictions with human interpretationsof the metaphorical utterances. Given a metaphorical utterance and a vague or specific goal condition, we computed themodel’s marginal posterior probabilities for f1 , f2 , and f3 . We
then correlate these posterior probabilities with participants’probability ratings from Experiment 2. Figure 2 plots modelinterpretations for all metaphors, features, and goal conditions against human judgments. Correlation across the 192
items (32 metaphors × 3 features × 2 goal conditions) is 0.6(p < 0.001). The predicted reliability of participants’ ratings
using the Spearman-Brown prediction formula is 0.828 (95%CI = [0.827, 0.829]), suggesting first that people do not agree
perfectly on metaphorical interpretations, and second that ourmodel captures a significant amount of the reliable variance in

0.4

0.5

0.6

0.7

Model

Figure 2: Model predictions (x axis) vs participants’ probability ratings (y axis) for 192 items (32 metaphors × 3 features
× 2 goal conditions). Shape of points indicates goal conditionand color indicates feature number.
the behavioral data. In particular, our model does especiallywell at predicting participants’ judgments of f1 , which are
the most salient features of the animal categories and weretargeted by specific questions in Experiment 2. Correlation
between model predictions and human judgments for f1 is 0.7(p < 0.0001), while the predicted reliability of participants’
ratings for f1 is 0.82 (95% CI = [0.818, 0.823]).
We now compare our model’s performance to a baselinemodel that also considers the feature priors and the conversational context. We constructed a linear regression modelthat takes the marginal feature priors for the animal category,the marginal feature priors for the person category, and thevague or specific goal as predictors of participants’ ratings.
With four parameters, this model produced a fit of r = 0.45,
which is significantly worse than our model (p < 0.0001 on
a Cox test). This suggests that our computational model adequately combines people’s prior knowledge as well as principles of pragmatics to produce metaphorical interpretations
that closely fit behavioral data.
While our model predictions provide a close fit to behavioral data, some residual variance can be further addressed.
Previous work has shown that alternative utterances—what
the speaker could have said—can strongly affect listeners’ interpretation of what the speaker did say (Bergen et al., 2012).
Our model currently does not take into account the range ofalternative utterances (both literal and metaphorical) that alistener considers when interpreting a speaker’s utterance. We
posit that this may account for some of the variance in the datathat our model does not capture. Consider the metaphor “Heis an ant” and the corresponding features small, strong, andbusy. Our model currently assigns a high probability to thefeature strong given the metaphor, while participants assign
it a lower probability. Indeed, this data point has the highest residual in our model fit. To demonstrate that alternative

utterances may account for this discrepancy, we construct amodel that has “He is an ox” as an alternative utterance. “Ox”
has features that roughly align with the features of “ant”:
strong, big, and slow. Since strong is a higher probability
feature for “ox” than for “ant,” the listener reasons that if the
speaker had intended to communicate the feature strong, shewould have said “He is an ox” since it optimally satisfies that
goal. Since the speaker did not produce the utterance “He isan ox,” the listener infers that strong is a less probable feature.
Adding this alternative utterance to the model indeed lowers
the marginal posterior probability of strong given the utterance “He is an ant.” As a result, we posit that adding alternative utterances across all animal categories may significantly
improve model performance. Constructing a complete set ofalternative utterances using our current set of metaphors is notpossible because feature combinations are not aligned acrossanimal categories (i.e., different animal categories have different feature sets, and not all features are shared by multiple
animals). We aim to address the role of alternative utterances
more specifically in future work.

Discussion
We have presented a computational model that predicts rich
metaphorical interpretations using general communicative
principles. Besides going beyond the literal meaning of anutterance to infer non-literal interpretations (e.g., John is aperson and not a shark), our model provides quantitative judgments about the person’s features (e.g., John is very likely
scary, dangerous, and mean). Furthermore, behavioral results
show that the interpretation of a metaphor is shaped in partby the conversational context, which our model naturally accounts for using communicative goals. Together these results
suggest that basic principles of communication may be an important driver of metaphor understanding.
Our model captures several intuitions about communication, including the importance of common ground between
listener and speaker, the context-dependence of communicative goals, and the idea that speakers choose to produce utterances that maximize informativeness about features relevantto their communicative goal. Each of these components inspire research questions that can be further investigated using
both our modeling framework and experimental paradigm.
For example, are listeners less likely to interpret an utterance metaphorically when there is little common ground between speaker and listener? What additional communicative
goals are metaphors able to satisfy more effectively than literal utterances? We aim to address these questions in future
research to further clarify how communication principles interact to produce metaphorical meaning. In addition, previous work has shown that conventional metaphors such as “He
is a pig” may be processed differently from novel metaphors(Bowdle & Gentner, 2005), which introduces a set of interesting questions to investigate with our model. We believe that
our computational framework advances understanding of thecomputational basis of metaphor and of communication moregenerally, and we hope that it will continue to shed metaphorical light on related questions.

Acknowledgments
This work was supported in part by NSF Graduate Research
Fellowships to JTK and LB and by a John S. McDonnell
Foundation Scholar Award and grants from the ONR to NDG.

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