Does Personality Matter?:
Expressive Generation for
Dialog Systems
IWSDS 2012
Natural Language and Dialogue Systems Lab
Prof. Marilyn Walker
Baskin School of Engineering
University of California, Santa Cruz
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Thank you for inviting me to this lovely
place!
Natural Language and Dialogue Systems Lab
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Expressive Language in Conversation
 Expresses Speaker’s Personality & Identity
 culture, style, origin, class
 Dynamically Adapts to Conversational Partner
 Convergent : Matching, e.g. two friends (extraverts)
talking
 Divergent: Tailoring, e.g. parent to baby
 Controlled by generation parameters




Content: Who is interested in what, who knows what
Linguistic: Lexical and Syntactic Choice
Pragmatic: Personality & Social Relationship
Acoustic: Speaking Rate, Amplitude, Prosody
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Personality in Language: People do it
Introvert
Extravert
I don't know man, it is
fine I was just saying I
don't know.
- I was just giving you a
hard time, so.
- I don't know.
- I will go check my e-mail.
- I said I will try to check
my e-mail, ok.
- Oh, this has been
happening to me a lot lately.
Like my phone will ring. It
won't say who it is. It just
says call. And I answer and
nobody will say anything.
So I don't know who it is.
- Okay. I don't really want
any but a little salad.
-
 From Mehl et al., 2006. Mairesse etal 2007.
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Film Characters: Crafted Personalities
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Does personality matter for dialogue
systems?
 Yes. Achieving communicative goals in dialogue often relies




on engaging user affect:
 persuasion, motivation, increase in self-efficacy beliefs,
learning
People react socially to computational agents, thus social
norms such as liking people like yourself often hold (Nass &
Lee, 2001)
People make attributions beyond social level: task
competence
Personality matching in a robotic exercise coach increased the
time that stroke victims spent on their medically recommended
exercises (Tapus & Mataric 2008)
Tutoring oriented to the student’s ‘face needs’ improved
learning in training and tutoring (Porayska-Pomsta & Mellish
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Dialogue Systems Architecture
Speech, Nonverbals
Text-toSpeech
Synthesis
Speech, Nonverbals
TTS
ASR
Data,
Rules
Words
Spoken
Language
Generation
Words
SLG
SLU
DM
Goal
Personality?
Speech
Recognition
Spoken Language
Understanding
Meaning
Dialogue
Management
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Outline of my talk
 Expressive natural language
generation
 PERSONAGE: personality models
for language generation (Mairesse &
Walker, UMUAI 2010; CL 2011, ACL2007; ACL
2008)
 Generating nonverbal expression
of personality (Bee, Pollack, Andre’ &
Walker IVA 2010; Neff, Wang, Abbott & Walker
IVA 2010, Neff, Toothman, Grant, Walker IVA
2011)
 Generation by learning models of film
characters from corpora (Lin & Walker
AIIDE 2011, Walker, Lin, Wardrip-Fruin, Buell,
Grant
ICIDSAND
2011)
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Limitations of previous work
 Writing character dialogue is an art: it is not described at a
level that supports computational models
 User Experience design is largely based on intuition
 Work on narrative (arts and humanities) does not suggest specific
linguistic or behavioral reflexes or parameters
 There has been little systematic exploration of personality
or social parameters suggested by psycholinguistic
findings
 Unclear which psycholinguistic findings have impact in
particular application domains
 Almost no evaluation showing variation system produces
is perceived by the user as the system intended
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Need a general purpose generation
technology that can be easily ported from
one domain/task to another. Expressive
but also for mixed initiative dialogue (any
branching dialogue).
10
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Procedural Language Generation: A Key
Technology
 Wide range of generation parameters
 Different methods for creating models
that control the parameters
 Dynamic Real-Time Adaptation
 Trainable: Machine Learning
Techniques
 Individual Personalization
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Dialogue Systems Architecture
Speech, Nonverbals
Text-toSpeech
Synthesis
Speech, Nonverbals
TTS
ASR
Data,
Rules
Words
Spoken
Language
Generation
Words
SLG
SLU
DM
Goal
Personality?
Speech
Recognition
Spoken Language
Understanding
Meaning
Dialogue
Management
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Language Generation Module
Content
Planner
Sentence
Planner
What to say
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Surface
Realizer
Prosody
Assigner
Speech
Synthesizer
How to Say It
What is Heard
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Variation controlled by the Language
Generator
Parametrized
Variation
Content
Planner
What to say
Sentence
Planner
Surface
Realizer
How to Say It
Prosody
Assigner
Speech
Synthesizer
What is Heard
• vary
content and form easily depending on any
factor (context, personality, social relationship)
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Expressivity?: Which parameters and
models?




Theories and Corpus Studies of Human Dialogue Behavior
Psychology: Big Five Theory of Personality
Sociolinguistics: Politeness Theory
Learn from Film Character Dialogue
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PERSONAGE Generator: BIG FIVE
Theory
 Conscientiousness: Dutiful vs. impulsive
 Emotional stability: Calm vs. anxious
 Openness to experience: Imaginative vs.
conventional
 Agreeableness: Kind vs. unfriendly
 Extraversion: Sociable, assertive vs. quiet
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Linguistic Reflexes of Personality: 50 yrs of
studies
 Extraversion (Furnham, 1990)
 Talk more, faster, louder and more repetitively
 Fewer pauses and hesitations
 Lower type/token ratio
 Less formal, more references to context (Heylighen &
Dewaele, 2002)
 More positive emotion words (Pennebaker & King, 1999)
 E.g. happy, pretty, good
 Neuroticism (Pennebaker & King, 1999)
 1st person singular pronouns
 Negative emotion words
 Conscientiousness (Pennebaker & King, 1999)
 Fewer negations and negative emotion words
 Low but significant correlations
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PERSONAGE Architecture: 67 Parameters
INPUT: Dialog Act,
Content Pool
Syntactic
Template
Selection
Content
Planner
OUTPUT
UTTERANCE
Aggregation
CONTRAST: e.g.
VERBOSITY
however, but
RESTATEMENTS
JUSTIFY: e.g.
CONTENT POLARITY
so, since
…
SYNTACTIC
COMPLEXITY
PERIOD
…
SELF-REFERENCE
…
Pragmatic
Marker
Insertion
Lexical
Choice
Realization
FREQUENCY OF USE
EXCLAMATION
WORD LENGTH
HEDGES: e.g. kind of, VERB STRENGTH
rather, basically, you know
FILLED PAUSES: e.g. err…
SWEAR WORDS: e.g. damn
IN GROUP MARKERS: e.g. pal
STUTTERING: e.g. Ri-Ri-River
TAG QUESTIONS
…
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Example of Pragmatic Transformation
 Negation insertion
 “X has awful food”  “X doesn’t have good food”
have
have
class: verb
Subj
Obj
Wok Mania
food
class: proper noun
number: sg
class: noun
number: sg
article: none
- Negate verb
- Replace adjective
by antonym
class: verb
negated: true
Subj
Obj
Wok Mania
food
class: proper noun
number: sg
class: noun
number: sg
article: none
ATTR
ATTR
awful
WordNet
Database
good
class:adjective
Look for antonym
class:adjective
“good”
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Recommendation: A Persuasive Task
Alt
Realization
Extra
5
Err... it seems to me that Le Marais isn’t as bad as the others.
1.83
4
Right, I mean, Le Marais is the only restaurant that is any good.
2.83
8
Ok, I mean, Le Marais is a quite french, kosher and steak house place, you know and
the atmosphere isn’t nasty, it has nice atmosphere. It has friendly service. It seems to
me that the service is nice. It isn’t as bad as the others, is it?
5.17
9
Well, it seems to me that I am sure you would like Le Marais. It has good food, the food
is sort of rather tasty, the ambience is nice, the atmosphere isn’t sort of nasty, it
features rather friendly servers and its price is around 44 dollars.
5.83
3
I am sure you would like Le Marais, you know. The atmosphere is acceptable, the
servers are nice and it’s a french, kosher and steak house place. Actually, the food is
good, even if its price is 44 dollars.
6.00
10
It seems to me that Le Marais isn’t as bad as the others. It’s a french, kosher and steak
house place. It has friendly servers, you know but it’s somewhat expensive, you know!
6.17
2
Basically, actually, I am sure you would like Le Marais. It features friendly service and
acceptable atmosphere and it’s a french, kosher and steak house place. Even if its
price is 44 dollars, it just has really good food, nice food.
6.17
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Training Models: Human Perceptions. 4
methods
 Rule based: take the findings from the psych
literature and encode them in a model
 Overgenerate and Rank: generate many
possibilities, collect human perceptual ratings,
learn to select ones that match the human
perception you are trying to achieve
 Parameter Estimation: Assume parameters
are independent and learn to set them as a
function of personality desired
 Film Corpora: Learn models from film
dialogue (Mairesse&Walker07, Mairesse & Walker UMUAI 2010,
Mairesse&Walker08, Lin&Walker11, Walkeretal11,Neffetal10, Neffetal11)
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Corpus Annotation: 3 Human Judges
(Ten-Item Personality Inventory, Gosling et al. 03)
Extraversion = 3.5
Neuroticism = 2.0
Agreeableness = 6.5
Conscient. = 4.0
Openness = 1.5
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1st method: Rule-Based Extraversion
Generation
 Use correlations in literature to set parameters
 Significant perceptual differences p < .01
 As binary classification, 90% accuracy
40
Introvert
Extravert
Utterance
count
30
20
10
0
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
Extraversion rating
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2nd Method: Overgenerate and Rank
Generator
“WokMania is a great
place, because the
food is good, isn’t it?”
Feature vector 1
Input
personality
score
e.g. 2.5 out of 7
“Err… this restaurant’s
not as bad the others.”
Feature vector 2
“Yeah, even if WokMania
is expensive, the food is
nice, I am sure you would
like WokMania!”
…
Feature vector 3
Feature vector n
Statistical
Regression/Ranking Model
Estimates scores from features,
e.g. verbosity, hedges
Closest estimate, utterance 2:
“Err… this restaurant’s not as bad the others.”
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Statistical Ranking Models of Personality
 Data: 160 random utterances rated by 3 judges
 Features based on generation decisions
 Random utterances less natural
than extreme utterances
 Results for predicting extraversion
 Linear regression, model trees, SVM
Correlation
Abs. error (1 to 7)
Extraversion
.60
.65
Emotional stability
.51
.82
Agreeableness
.58
.67
Conscientiousness
.38
.84
Openness to experience
.39
.92
 Better than inter-rater correlations for random utterances (r = .40)
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Output
Extraversion
Score
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Linear regression model for Agreeableness
Agreeableness score =
1.9614
0.919
0.692
0.593
0.432
0.414
0.326
0.314
0.305
…
+
*
*
*
*
*
*
*
*
Content Plan Tree:Content Polarity +
RST Tree Modification:Polarisation +
Hedge Insertion:down_subord_it_seems_to_me_that +
Hedge Insertion:in_group_marker=1 +
Hedge Insertion:down_err=0 +
Tag Question Insertion=0 +
Hedge Insertion:down_somewhat=0 +
Hedge Insertion:swearing=0 +
-0.0878
-0.0915
-0.1942
-0.2004
-0.2099
-0.3284
-0.6367
*
*
*
*
*
*
*
Demonstrative Referring Expressions +
Content Plan Tree:Repetitions Polarity +
Content Plan Tree:Concessions +
Positive Content First +
Aggregation Operation Probabilities:contrast:PERIOD+
Hedge Insertion:down_subord_it_seems_that +
Hedge Insertion:competence_mitigation_come_on
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Third Method: Parameter Estimation
Models
 Data: 160 randomly generated utterances + generation
decisions+ ratings
 Training Multiple Continuous Parameters Models
 Independence assumption between parameters
 Best regression models selected through cross-validation
 Example: Stuttering
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Utterances express *multiple* personality
traits
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Naïve Subjects Evaluation
Experiment
 24 subjects rated 50 utterances
 Each utterance hits a combination of
Big Five targets
Extraversion = 3.5
Neuroticism = 1.7
Agreeableness = 6.5
Conscientiousn. = 4.0
Openness = 4.5
 Correlation between target scores and average ratings
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So where are we?





A flexible, real-time, generator
Personality parameters
Methods for automatically training
Personalize both content and form
Standard meaning representations: DB Relations,
Content Plan, AI planner
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There are also findings in psychology about
how VOICE and FACE and GESTURE
express personality. Can we use them?
Natural Language and Dialogue Systems Lab
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Psychological Correlates Between Extraversion and
Movement
Introversion
Extraversion
Body attitude
backward leaning, turning away forward leaning
Gesture amplitude
narrow
wide, broad
Gesture rate
low
high
more movements of head, hands
and legs
Gesture speed,
response time
slow
fast
Gesture direction
more inward, self-contact
more outward, table-plane and
horizontal spreading gesture
Gesture connection
low smoothness, rhythm
disturbance
smooth, fluent
Body part
shoulder erect, limbs spread,
elbows away from body, hands
away from body, legs apart
(References in Neff etal, 2010, 2011)
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Alfred: Facial Dominance Behaviors &
Personality
Hypoth: Persuasiveness could be increased by
expressing dominance with personality (Marwell, 1967,
Mehrabian, 1995)
Extravert, Hi Dominance
Introvert, Low Dominance
Bossy or Wimpy: Expressing Social Dominance by Combining Gaze and Linguistic
Behaviors (Bee, Pollack, Andre’ & Walker, Intelligent Virtual Agents 2010)
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Evaluating the Effect of Gesture and
Language on Personality Perception in
Conversational Agents
Natural Language and Dialogue Systems Lab
Intelligent Virtual Agents, 2010
Michael Neff, Yingying Wang(UCD), Rob Abbott, Marilyn
Walker(UCSC)
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Experimental Set-up
Rate: (on seven point scale)
1.Extraverted, enthusiastic:
2.Reserved, quiet:
3.Naturalness
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ooooooo
ooooooo
ooooooo
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So where are we now?
 Voice realization maintains personality
perceptions
 Methodology of mining social psychology
literature for parameters extends to gaze, head
position, body and arm gestures
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Character Creator: Author creativity
 Learn models of character voice
(linguistic style) from film
screenplays
 Use the learned models to control
the parameters of PERSONAGE
 Apply the learned models to
character dialogue in the SpyFeet
story domain
 A Different!! Domain
 Test human perceptions of the
resulting generated utterances
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Annie Hall: Getting a lift
Scene from Annie Hall: Lobby of Sports
Club
ALVY: Uh … you-you wanna lift?
ANNIE: Turning and aiming her thumb
over her shoulder
Oh, why-uh … y-y-you gotta car?
ALVY: No, um … I was gonna take a
cab.
ANNIE: Laughing Oh, no, I have a car.
ALVY: You have a car?
Annie smiles, hands folded in front of
her
So … Clears his throat. I don’t
understand why … if you have a car, so
then-then wh-why did you say “Do you
have a car?” … like you wanted a lift?
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The Terminator:
getting a lift
Scene from The Terminator: Cigar biker
TERMINATOR: I need your clothes, your boots, and your motorcycle.
CIGAR BIKER: You forgot to say please.
Terminator hurls Cigar, all 230 pounds of him, clear over the bar, through
the serving window into the kitchen, where he lands on the big flat
GRILL. We hear a SOUND like SIZZLING BACON as Cigar screams,
flopping jerking. He rolls off in a smoking heap.
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What can we learn from a corpus?
 Reveal Subtext: The way a character says
something is one way to reveal subtext and
character emotion
 Short vs. Long turns/sentences => friendliness,
formality
 Word choice => level of education,
 Disfluencies, Stuttering => anxiety, hesitation
 Direct forms vs. indirect forms => extraversion,
aggression
 Character Voice: Learning to model specific
characters or sets of characters should produce
individual character voices
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SPYFEET: Dynamic NPC/Player dialogue
 Role playing game
 Solve a mystery by
talking to ‘animal
spirits’
 Hypotheses: Dynamic
elements will increase
engagement &
immersion
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LANGUAGE
AND
Natural
Language and
Dialogue Systems
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http://nlds.soe.ucsc.edu
Method
1. Collect
movie scripts
from IMSDb
4. Generate
features
reflecting
linguistic
behaviors
2. Extract
utterances for
each character
Vincent’s
Dialogue
…
Pulp Fiction
Script
Other’s
Dialogue
NATURAL LANGUAGE AND DIALOGUE SYSTEMS
Jules’
Vincent’s
Dialogue
Dialogue
Jules’ Tag
Question
Vincent’s Tag
Ratio
Question
Ratio
Jules’
Overall
Vincent’s
Polarity
Overall
Polarity
…
Jules’
Dialogue
3. Select
leading roles
(dialogue > 60
turns)
Jules’
LIWC
results
Vincent’s
LIWC results
Jules’
other
Vincent’s
features
other
features
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Method (cont)
5. Learn models
of character (zscores)
Generated
features
Jules’
Vincent’s
Learned
Learned
Model
Model
6. Generate new
utterances using learned
models to control
parameters of our dialogue
generator
Jules’ in
SpyFeet
utterances
Story domain:
SpyFeet
utterances
NATURAL LANGUAGE AND DIALOGUE SYSTEMS
Vincent in
SpyFeet
utterances
…
PERSONAGE
generator
(ENLG
engine)
Others in
SpyFeet
utterances
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Learning Character Models: Z-Scores
Z-scores: individual models trained by normalizing individual
character model against a representative population
Example: normalize Annie (Annie Hall) against all female
characters
Annie’s vector
Annie’s
z-score


Averaged female population
Standard deviation female population
z-score >1 or <-1 is more than one standard deviation
away from the average
Indicates parameters that should be high or low
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Example: Model Learned for Annie
Map character model to PERSONAGE parameters: weighted average of
features.
Parameters either binary, or scalar range 0…1.
PERSONAGE
parameter
Description
Sample mapped features
(from character model)
Annie
Verbosity
Control # of propositions in the
utterances
Number of sentences per turn,
words per sentence
0.78
Content
polarity
Control polarity of propositions
expressed
Polarity-overall, LIWC-Posemo,
LIWC-Negemo, LIWC-Negate
0.77
Polarization
Control expressed polarity as
neutral or extreme
1 if polarity-overall is strong
negative or positive
0.72
Concessions
Emphasize one attribute over
another
Category-concession
0.83
Positive
content first
Determine whether positive
propositions – including the
claim – are uttered first
Accept-ratio, Accept-firstratio
1.00
… etc.
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Original and Generated Utterances
Annie (Annie Hall)
original dialogue sample
• H’m?
That’s, uh … that’s pretty
serious stuff there.
Yeah? Yeah?
M’hm? M’hm.
Yeah.
Learning
U-huh.
Linguistic
• Hi. Hi, hi.
Features
Well, bye.
Oh, yeah? So do you.
Oh, God, whatta- whatta
dumb thing to say, right?
I mean, you say it, “You
play well,” and right
away … I have to say
well. Oh, oh … God,
Annie. Well … oh, well …
la-de-da, la-de-da, la-la
Annie’s Learned Z-Score
Model for our ENLG
engine
Verbosity=0.78
Content polarity =0.77
Polarization =0.72
Repetition polarity=0.79
Concessions =0.83
Concessions
Polarity=0.26
Positive content first=1.00
First Person in Claim=0.6
Claim Polarity=0.57
… etc.
NATURAL LANGUAGE AND DIALOGUE SYSTEMS
Generation
Generated dialogue
(SpyFeet story domain)
• Come on, I don’t
know, do you? People
say Cartmill is strange
while I don’t rush to
um.. judgment.
• I don’t know. I think
that you brought me
cabbage, so I will tell
something to you,
alright?
• Yea, I’m not sure,
would you be? Wolf
wears a hard shell but
he is really gentle.
• I see. I am not sure.
Obviously, I respect
Wolf. However, he
isn’t my close friend, is
he?
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Perceptual Experiment Part 3: Can People
Tell?
Example: read 3 scenes for Marion, then read 6 sets of
generated utterances to determine similarity of style to
original utterances
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Hypoth 2: Character Models Perceived?
Average similarity scores (1 to 7) between character and character
models.
Perfect Performance: A matrix with highest values along
diagonal
Character
Character Models
Alvy
Annie
Indy
Marion
Mia
Vincent
Alvy
5.2
4.2*
2.1*
2.6*
2.8*
2.3*
Annie
4.2
4.3
2.8*
3.4*
3.9
2.9*
Indy
1.4*
2.2*
4.5
2.8*
3.3*
3.8*
Marion
1.6*
2.8*
3.7
3.1
4.1*
4.2*
Mia
1.7*
2.4*
4.3
3.2
3.6
4.3
Vincent
2.1*
3.2*
4.5
3.5*
3.6*
4.6
*significant differences between character and character models of
each row
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Summary & Future Work
 Personality for dialogue agents can help achieve
many conversational goals
 Current & Future work:
 Methods to make it easy to get content in new domains
into generator
 Experiment to test whether helps people authoring
interactive stories
 Evaluate use of different personalities for in-vehicle task
dialogue demo
 Would like to test in a long-term companion scenario
either phone, car or eldercare robot
NATURAL LANGUAGE AND DIALOGUE SYSTEMS
UC SANTA CRUZ
Come Visit !! University of California Santa
Cruz
NATURAL LANGUAGE AND DIALOGUE SYSTEMS
UC SANTA CRUZ
Alfred Experiment: Gaze & Personality
NATURAL LANGUAGE AND DIALOGUE SYSTEMS
UC SANTA CRUZ
Mary: Gesture & Personality
NATURAL LANGUAGE AND DIALOGUE SYSTEMS
UC SANTA CRUZ