“I won’t lie, it wasn’t amazing”: Modeling polite indirect speech
Erica J. Yoon, Michael Henry Tessler, Noah D. Goodman and Michael C. Frank{ejyoon, mtessler, ngoodman, mcfrank} @stanford.edu
Department of Psychology, Stanford University
Abstract
Why are we polite when we talk to one another? One hypothesis is that people expect others to choose what to say basedon their goals both to transfer information efficiently (an epistemic goal) and to make the listener feel good (a social goal).
In our previous work, we found that when these two goals conflict, they sometimes produce white lies. In the current work,
we expand on this theory to consider another prominent case ofpolite speech: indirect remarks using negation (e.g., “It wasn’t
amazing”). With minimal extensions from our previous framework, our formal model suggests that a pragmatic speaker willproduce more indirect remarks when the speaker wants to be
informative and seem considerate at the same time. These
predictions were borne out in a language production experiment. These findings suggest that the conflict between socialand epistemic goals can account for a broad range of politenessphenomena.
Keywords: Politeness; computational modeling; communicative goals; pragmatics

Introduction
Language users hear and produce polite speech on a daily
basis. Adults and even young children spontaneously produce requests in polite forms (Axia & Baroni, 1985; Clark& Schunk, 1980), and speakers use politeness strategies evenwhile arguing, preventing unnecessary offense to their interactants (Holtgraves, 1997). But being polite conflicts with
one important goal of cooperative communication: exchanging information efficiently and accurately (Grice, 1975). People tell white lies (“Your new dress is gorgeous!”) and produce indirect speech that is longer and more nuanced than thesimplest form of their intended message (“I don’t think thatdress looks phenomenal on you” as opposed to “It looks terrible”) to make others feel good. Speakers risk potential lossof their intended message (indirect speech), intentionally convey wrong information (lies), and suffer inefficiencies – all inthe service of being polite. If information transfer were theonly currency in communication, politeness would be bothinfelicitous and undesirable.
A cooperative speaker, however, can be imagined as one
with both an epistemic goal to improve the listener’s knowledge as well as a social goal to minimize potential damage tothe hearer’s (and the speaker’s own) self-image, called face
(Brown & Levinson, 1987). If the speaker’s intended meaning contains no threat to the speaker or listener’s face, then the
speaker will choose to convey the meaning in an explicit andefficient manner (putting it “on the record”). As the degreeof face-threat becomes more severe, however, a speaker will
choose to be polite by producing more indirect utterances.
Inspired by this set of ideas, we have argued that listeners
think about polite speech as reflecting a tradeoff between two
goals: information transfer (which we called epistemic utility) and face-saving (social utility; Yoon, Tessler, Goodman,& Frank, 2016). A speaker with a high weight on social utility will try to save her listener’s face: She hides or risks losing
information in her intended message by making her utterance
false to some degree. On the other hand, a speaker with ahigh weight on epistemic utility prioritizes truthfulness andinformativity, and she may risk a loss of the listener’s (or the
speaker’s own) face. These ideas were formalized in a modelof pragmatic language understanding, building on the Rational Speech Act (RSA) theory (for a review, see Goodman &Frank, 2016). We tested the polite RSA model (pRSA) by examining white lies. The model captured human participants’
inferences about a speaker’s goals given her utterance (e.g.,saying a good talk was “amazing” implies that she is beingnice) and about the true state of the world given a speaker’s
goal (e.g., saying “good” may mean the talk was only okay if
the speaker wanted to be nice).
In the current work, we extend our framework to anotherpolite speech act: indirect speech. White lies are produced
when a speaker tries to save the listener’s face by stretching the truth. But instead of lying, people sometimes try to
be polite by being more indirect. Through indirect speech,a speaker can express meaning that is different from the literal meaning of the utterance (Searle, 1975). In this work, we
focus on negation (“not”), which has the potential to be indirect. For instance, “Mark isn’t the cleanest person I know”
may suggest that the speaker thinks Mark is unclean (inferredmeaning) rather than not being the person who has the greatest degree of cleanliness (literal meaning). Negation can beused as a hedging or mitigating device to address an undesirable state that is face-threatening to the addressee (Brown &Levinson, 1987; Grice, 1975).
What may lead a speaker to produce indirect remarks? An
indirect remark may be motivated by the speaker’s goal toconvey some face-threatening information, while being seen
as a polite person who avoids threatening others’ face. In ourprevious work, we described a pragmatic listener that jointlyinferred the true state and the goals of the speaker. Building on this model, we describe here a speaker whose goal is
to lead this pragmatic listener to infer the true state and attribute to the speaker certain goals (e.g., face-saving). For
instance, “It wasn’t amazing” does not preclude the possibility that the presentation was bad, and may in fact be pragmatically strengthened to mean that it was actually bad. Yet
because the speaker does not choose the more direct “It wasbad”, the listener will infer a face-saving goal. Thus saying“It wasn’t amazing” can accomplish the goal of conveyingthat the presentation was bad while the speaker is seen as notwanting to make the listener feel bad. On the other hand, if
the speaker does not care about being seen as face-saving,she will produce less indirect speech. Further, if the presentation was actually good, or even decent, the speaker will prefer to produce a directly positive remark (“It was good”) ineither case. Thus we predict more indirect speech when thetrue state is bad, and an interaction with the speaker’s desire
to both be informative and be seen as wanting to save face.
In what follows, we derive our hypotheses using our formal
model and present an empirical test of the hypotheses.

Computational ModelIn the current work, we introduce a minimal extension to ourprevious RSA model (pRSA; Yoon et al., 2016) to allow for
speaker production of indirect remarks using negation.

Polite RSA
RSA models assume speakers choose utterances approximately optimally given a utility function (Goodman &Stuhlmuller, 2013). pRSA posited that the speaker’s utility
function can be decomposed into two components. First,
epistemic utility (Uepi ) refers to the standard, informative
utility in RSA: the amount of information a literal listener(L0 ) would still not know about world state s after hearing
a speaker’s utterance w. Second, social utility (Usoc ) is the
expected subjective utility of the state inferred given the utterance w. The expected subjective utility is related to theintrinsic value of the state, and we use a value function (V )to map states to subjective utility values. This captures theaffective consequences for the listener of being in state s. Finally, some utterances might be costlier than others. The utility of an utterance subtracts the cost c(w) from the weighted
combination of the social and epistemic utilities.
ˆ = βepi · ln(PL (s | w)) + βsoc · EP (s|w) [V (s)] −C(w)U(w; s; β)0L0The speaker (S1 ) in pRSA chooses utterances w softmaxˆ The pragoptimally given the state s and his goal weights β.
matic listener (L1 ) jointly infers the state s and the utility
weights of the speaker, βepi and βsoc (Goodman & Lassiter,
2015; Kao, Wu, Bergen, & Goodman, 2014).

Extensions to pRSA
We build on pRSA by adding negative utterances and modeling a more sophisticated speaker. First, we extend the utterance alternatives to include negation. Previously we considered five possible utterances: {It was terrible, bad, okay,
good, and amazing}, all direct assertions of specific states(e.g., “It was amazing” would be true for the state of 5 but
untrue for the states of 1 or 2). Now the speaker may say, {Itwasn’t terrible, bad, okay, good, and amazing}. These utterances indirectly address the referent by negating certain state.
We assume that it is more costly to say utterances with negation, which makes the utterance morphemically longer andis harder to process (Clark & Chase, 1972). In our full data
analysis, we put a prior on this negation cost parameters andinfer its likely values from the data.
Most importantly, we extended the recursive reasoning in
the model. For our experiment, we consider the pragmatic
speaker (S2 ) who chooses an utterance based on the pragmatic
listener model (Eq. 1), thinking about the state as well as goal
weights that the pragmatic listener will infer.
ˆ ∝ exp(λ2 · ln(PL (s, βˆ | w)) −C(w))
PS2 (w | s, β)1This crucially captures the idea that the speaker both wants
to convey the state s, and to be seen as someone with goals
ˆ We simplify from the Yoon et al. (2016) model by inβ.
cluding only a single mixture parameter φ governing the extent to which the speaker is being informative vs. face saving:
βepi = φ, βsoc = 1 − φ.
We implemented this model using the probabilistic programming language WebPPL (Goodman & Stuhlmuller,2014)1 . In the next section, we explore the model’s predictions for speaker productions of indirect speech with negation
vs. direct speech with no negation.

Model predictions

1(2)

Before describing our experimental data, we derive predictions from the pRSA model. In these initial simulations, we
use fixed goal weights and parameters – in later fits, we will
derive these parameters from the data using Bayesian data
analysis. Since the model requires measurements of literalsemantics (e.g., what “not good” means on a given dimension), we first describe these measurements and then givemodel predictions using them.

PL0 (s | w) ∝ [[w]](s) · P(s)(3)

Semantic measurement

ˆ · P(s) · P(β)ˆ
PL1 (s, βˆ | w) ∝ PS1 (w | s, β)ˆ ∝ exp(λ1 · E[U(w; s; β)])ˆPS (w | s, β)(1)Within our experimental domain, we assumed there were
five possible states of the world corresponding to the value
placed on a particular referent (e.g., rating deserved by thepresentation the speaker is commenting on, akin to a Yelp rating): S = {s1 , ..., s5 }. We assume a uniform prior distribution
over possible states of the world. The states have subjective
numerical values V (si ) = α · i, where α is a free parameter.
[[w]](s) corresponds to the lexical meaning of the utterance w(e.g., “good”) when applied to state s. We gather independent
ratings for these literal meanings.

We probed judgments of literal meanings of the target words
assumed by our model and used in all our experiments.
Materials, methods, and results 25 participants with IP
addresses in the United States were recruited on Amazon’s
Mechanical Turk. We used 13 different context items thatwere previously used in Yoon et al. (2016), in which someone
1 A complete implementation of the model, raw data and anal-

yses, and links to the experiments and pre-registration of hypotheses and method can be found at https://github.com/ejyoon/
cogsci2017.

proportion
acceptances

terrible

bad

okay

good

amazing

1.00
0.75
it was ~0.50
it wasn't ~

0.25
0.00
1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

state (1=worst)

Figure 1: Semantic measurement results. Proportion of acceptances of utterance types (colors) combined with target words(facets) given the true state represented on a scale of hearts. Error bars represent 95% confidence intervals.

Figure 2: Schematic model predictions (left), experimental results (center) and fitted model predictions (right) for average
proportion of negation produced among all utterances, given true states (x-axis) and goals (colors).
evaluated a performance of some kind. For example, in oneof the contexts, Ann saw a presentation, and Ann’s feelings
toward the presentation (true state) were shown on a scale outof five hearts (e.g., two out of five hearts filled in red color).
The question of interest was “Do you think Ann thought thepresentation was / wasn’t X?” and participants responded bychoosing either “no” or “yes.” The target could be one of fivepossible words: terrible, bad, okay, good, and amazing, giving rise to ten different possible utterances (with negation orno negation). Each participant read 50 scenarios, depicting
every possible combination of states and utterances. The order of context items was randomized, and there were a maximum of four repeats of each context item per participant.
For this and subsequent experiments, we analyzed the databy collapsing across context items.

For each utterance-state pair, we computed the posterior
distribution over the semantic weight (i.e., how consistent X
utterance is with Y state) assuming a uniform prior over theweight. Meanings of the words as judged by participants wereas one would expect (see Figure 1). We used the fraction ofparticipants that endorsed utterance w for state s to set informative priors to infer posterior credible values of the literalmeanings from data in the speaker production experiment.

Model parameters and predictions
The S2 speaker in our model has the goal to convey the state
and to be seen as having a particular set of goals. We explore predictions for 3 hypothetical speakers, corresponding
to 3 different φ mixture parameter weights: (a) an informative
speaker who wants to convey high epistemic utility (prioritizing information transfer; φ = 0.9) (b) a social speaker who
wants to convey high social utility (making the listener feel
good; φ = 0.1) (c) a both-goal speaker who wants to convey
a balance between the two utilities (φ = 0.5).2
Figure 2 (left) shows the speaker’s production probabilities
associated with producing an indirect speech act (i.e., an utterance with negation) for the three different speakers as thetrue state of the world is varied. We see, consistent with ourintuition, that indirect speech was relatively more preferred inbad states than in good states. As well, we see higher probability of negation production for the speaker who wants toconvey both goals (epistemic and social) relative to each goal
2 In addition, the model has a few parameters not of theoreticalinterest. For the purposes of generating model predictions a priori,
we assign values to these parameters consistent with the previous
literature with this class of models: the speaker optimality parameter(λ1 assigned to 2); the pragmatic speaker optimality parameter (λ2to 2); the value scale parameter (α to 1) in the utility function; andthe parameter governing the cost of producing a negation (C(u) to2).

independently. Indirect speech does not convey that much information and so the informative speaker (a) would disprefer
it. The social speaker (b) who wants to convey a face-saving
goal would tend to signal a better-that-actual state throughdirect positive remarks. The both-goal speaker produces indirect remarks to avoid direct remarks that are either true but
face-threatening, or face-saving but false.

Speaker production experiment
To compare against our model predictions, we measured participants’ predictions for the most likely utterance (w) produced by the speaker, given a description of the true state.
For example, given that Ann wanted to make Bob feel good
but felt that his poem deserved 2 out of 5 hearts, what would
she say? We hypothesized that when there was no tradeoff
between informativity and face-threat avoidance (i.e., when
the addressee’s performance was great), speakers would use
truthful and face-saving direct remarks (“[Your poem] wasamazing”) regardless of their described goals. However,when there was a conflict between the epistemic and social
goals (i.e., when the addressee’s performance was poor), a
speaker who tried to convey both goals would use vague indirect remarks (“[Your poem] wasn’t terrible”) more often thandirect face-threatening remarks (“[Your poem] was bad”; preferred by a speaker who only considered the epistemic goal)or direct face-saving remarks (“[Your poem] was good”; preferred by a speaker who wanted to convey only a social goal).

MethodParticipants 202 participants with IP addresses in the
United States were recruited on Amazon’s Mechanical Turk.
Stimuli and Procedure As in the semantics measurements
above, we used scenarios in which a person (e.g., Bob) gavesome performance and asked for another person (e.g., Ann)’s
opinion on the performance. Additionally, we provided information on the speaker Ann’s goal – to make Bob feel good, or
to give as accurate and informative feedback as possible, orboth – and the true state – how Ann actually felt about Bob’s
performance (e.g., 2 out of 5 hearts). Each participant read15 scenarios, depicting every possible combination of goals
and states. The order of context items was randomized, andthere were a maximum of two repeats of each context itemper participant.
Each scenario was followed by a question that read, “If
Ann wanted to make Bob feel good but not necessarily giveinformative feedback (or to give accurate and informative
feedback but not necessarily make Bob feel good, or BOTH
make Bob feel good AND give accurate and informative feedback), what would Ann be most likely to say?” Participants
indicated their answer by choosing one of the options on
the two dropdown menus, side-by-side, one for choosing between was vs. wasn’t and the other for choosing among terrible, bad, okay, good, and amazing (see Figure 3).

Figure 3: Example of a trial in Experiment 1.

Behavioral results
Our hypotheses for utterance production by speakers with different goals were borne out (see full results in Figure 4).
For good states (4 and 5 hearts), positive direct remarks were
judged to be the most likely utterances across all three goal
conditions. For less-than-perfect, but still decent states, there
was a greater degree of expectation of white lies (e.g., “It wasamazing” for 4 hearts) given a social goal. For bad states(1 and 2 hearts), as predicted, there were more instances of
expected indirect remarks overall across all goal conditions
given bad states. Critically, speakers with both informativeand social goals produced more indirect remarks than wereobserved in the other two goal conditions (Figure 2, center).

Model results
Model fitting In this experiment, participants were told
what speakers’ intentions were (e.g., wanted to make Bob feelgood). We assume that the intention descriptions conveyed
the weight mixture φ that the speaker was using. We put uninformative priors on this mixture (φ ∼ Uniform(0,1)) and inferred their credible values separately for each goal condition(“wanted to X”) using Bayesian data analytic techniques (Lee& Wagenmakers, 2014). We also used the fraction of participants that endorsed utterance w for state s to set informative
priors to infer posterior credible values of the literal meanings
from data.
There were four additional parameters of the model, on
which we put uninformative priors: the speaker optimality
parameter (λS1 ∼ Unif(0,20)); the pragmatic speaker optimality parameter (λS2 ∼ Unif(0,5)); the value scale parameter(α ∼ Unif(0,5)) in the utility function; and the cost parameter (C(u) ∼ Unif(1,10)). We inferred their posterior credible
values from the data. We ran 4 MCMC chains for 80,000
iterations, discarding the first 40,000 for burnin. The Maximum A-Posteriori (MAP) estimate and 95% Highest Probability Density Interval (HDI) were: λS1 : 2.16 [2.02, 3.61];
λS2 : 0.91 [0.83, 1.75]; α: 2.71 [0.98, 4.59]; C(w): 2.04 [1.95,
2.25]. To generate utterance predictions, given our model andthe inferred parameters, we evaluated the posterior predictive
distribution, marginalizing out all parameters.

Figure 4: Experimental results (solid lines) and fitted model predictions (dashed lines) for speaker production. Proportionof utterances chosen (utterance type – direct vs. indirect – in different colors and words shown on x-axis) given the true
states (columns) and speaker goals (rows). Error bars represent 95% confidence intervals for the data and 95% highest density
intervals for the model.
Results The inferred weights for each goal condition werelargely as expected: For the “wanted to give informative feedback” (informative) condition, the model put a relatively highweight on epistemic utility (0.81). For the “wanted to make[listener] feel good” (social) condition, the model inferredthat the speaker was using a moderate weight on epistemic
utility (0.51). For the “wanted BOTH to make [the listener]
feel good and give informative feedback” (both) condition,
the model assigned a weight on epistemic utility between theweights for the other two goal conditions (0.57). Overall,
the weights tended to be more biased towards prioritizing the
epistemic utility.
The predictions for the speaker’s utterance were overall
highly consistent with the experimental findings (Figure 4).
The posterior predictive of the model explained almost allof the variance in the production data r2 (150) = 0.962 (Figure 5). The model successfully predicted distinct patterns foreach goal condition. The informative speaker produced direct
remarks whose literal meanings mapped onto the true states(e.g., “It was terrible” given 1 heart). The social speaker produced remarks that were positively biased compared to thetrue states (e.g., “It was okay” given 2 hearts).
While the model in the both condition did produce indirect utterances (e.g., “It wasn’t terrible” given 1 heart) it did
so slightly less than the empirical data. For this reason, themodel did not yield the expected difference for negation production between both-goal and social conditions (Figure 2,
right); though the trend was numerically correct, the effectwas much smaller in the fit model than the schematic one.
There are several possible explanations for this small deviation. In our experimental data, the social speaker placed ahigher weight on epistemic utility than in our schematic predictions. Thus, the particular goal descriptions we used in theexperiment may have suggested that the social speaker stillwanted to be seen as informative, and have led to little differentiation between the social vs both-goal speaker. Anotherpossible cause is that participants preferred a different kindof indirect speech than the model – in particular, the bothgoal speaker preferred to produce “It wasn’t amazing” in theschematic model predictions, whereas participants in our experiment chose “It wasn’t terrible.” This discrepancy between
the two remarks is interesting, because their implied meaning
is similar. In a pilot experiment where participants were asked
to infer the true state (number of hearts) from an utterance, “It
wasn’t amazing” and “It wasn’t terrible” were very similar (˜2hearts).

Discussion
Why are we polite? Here we explored a formal instantiationof the hypothesis that two conflicting speaker goals – epis-

the need for indirect speech results from the conflict between
these two. These findings provide strong support for a utilitytheoretic framing of politeness, and suggest new directions inunderstanding of pragmatic language use in social contexts.

Acknowledgments
This work was supported by NSF grant BCS 1456077 to
MCF, ONR grant N00014-13-1-0788 to NDG, NSF Graduate Research Fellowship DGE-114747 to MHT, and NSERCPGS Doctoral scholarship PGSD3-454094-2014 to EJY.

References

Figure 5: Full distribution of human responses vs. model
predictions. Error bars represent 95% confidence intervals
for the data (vertical) and 95% highest density intervals forthe model (horizontal).

temic and social – can be used to explain a range of polite
behavior, including white lies and indirect speech acts using negation. Our model predicted that speakers should produce more indirect remarks in cases of greater face threat(given the addressee’s poorer performance) and in cases
where speakers wanted to be both informative and nice. Our
experimental data confirmed these predictions. The model’s
overall fit to the data was very strong, although it did not show
the predicted dominance of indirect speech for the both-goal
speaker at low states. Whether this discrepancy between theinitial and data-fitted predictions was due to variation in goal
weight based on experimental scenarios or a discrepancy inpreferences for particular utterances is a question for futurework.
An important contribution of this work is in showing thegeneralizability of our formal model (pRSA) to the case ofindirect speech acts. The current work took a step in addressing speakers’ self-presentation: Not only do speakers wantto save the listener’s face, but they also want to save theirown face, by appearing informative and considerate to the listener. In future work we hope to explore this aspect more andtest how our model’s utilities can be extended to capture thespeaker’s desire to appear polite, genuine, and even modest.
Using the model to explore other kinds of polite speech suchas indirect requests (“Would you mind closing the window?”;
Clark & Schunk, 1980) and manifestations of polite speechin different cultures (e.g., Holtgraves & Joong-nam, 1990)
are also important future directions.
In sum, our formal model and experimental work represent
an advance in polite speech understanding. With a minimal
extension to our existing model, we were able to capture subtle patterns in people’s inferences about indirect speech production. Our empirical findings suggest that neither epistemic
nor social motives alone motivate indirect speech; instead,

Axia, G., & Baroni, M. R. (1985). Linguistic politeness at
different age levels. Child Development, 918–927.
Brown, P., & Levinson, S. C. (1987). Politeness: Some universals in language usage (Vol. 4). Cambridge Univ. Press.
Clark, H. H., & Chase, W. G. (1972). On the process of comparing sentences against pictures. Cognitive Psychology,
3(3), 472–517.
Clark, H. H., & Schunk, D. H. (1980). Polite responses topolite requests. Cognition, 8(2), 111–143.
Goodman, N. D., & Frank, M. C. (2016). Pragmatic language interpretation as probabilistic inference. Trends in
Cognitive Sciences, 20(11), 818–829.
Goodman, N. D., & Lassiter, D. (2015). Probabilistic semantics and pragmatics: Uncertainty in language and thought.
In S. Lappin & C. Fox (Eds.), The handbook of contemporary semantic theory, 2nd edition. Wiley-Blackwell.
Goodman, N. D., & Stuhlmuller, A. (2013). Knowledge andimplicature: Modeling language understanding as social
cognition. Topics in Cognitive Science, 5.
Goodman, N. D., & Stuhlmuller, A. (2014). The Design and
Implementation of Probabilistic Programming Languages.
http://dippl.org.
Grice, H. P. (1975). Logic and conversation. In Readings inlanguage and mind. Blackwell.
Holtgraves, T. (1997). YES, but. positive politeness in conversation arguments. Journal of Language and Social Psychology, 16(2), 222–239.
Holtgraves, T., & Joong-nam, Y. (1990). Politeness as universal: Cross-cultural perceptions of request strategies andinferences based on their use. Journal of Personality andSocial Psychology, 59(4), 719.
Kao, J. T., Wu, J. Y., Bergen, L., & Goodman, N. D. (2014).
Nonliteral understanding of number words. Proceedings of
the National Academy of Sciences, 111(33), 12002–12007.
Lee, M. D., & Wagenmakers, E. J. (2014). Bayesian cognitive modeling: A practical course. Cambridge Univ. Press.
Searle, J. R. (1975). Indirect speech acts. In P. Cole & J.
Morgan (Eds.), Syntax and semantics (vol. 3): Speech acts.
New York: Academic Press.
Yoon, E. J., Tessler, M. H., Goodman, N. D., & Frank, M. C.
(2016). Talking with tact: Polite language as a balance between kindness and informativity. In Proceedings of thethirty-eighth annual conference of the Cognitive Science
Society.