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NATURAL LANGUAGE AND DIALOGUE SYSTEMS
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Natural Language and Dialogue Systems Lab
Does Personality Matter?:
Expressive Generation for
Dialog Systems IWSDS 2012
Prof. Marilyn Walker
Baskin School of Engineering
University of California, Santa Cruz
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Natural Language and Dialogue Systems Lab
Thank you for inviting me to this lovely
place!
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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
From Mehl et al., 2006. Mairesse etal 2007.
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.
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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
2004; Wang et al., 2004)
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Dialogue Systems Architecture
DM
SLU
TTS
Text-to-
Speech
Synthesis
Spoken Language
Understanding
Dialogue
Management
ASR
SLG Spoken
Language
Generation
Data,
Rules
Words
Meaning
Speech, Nonverbals Speech, Nonverbals
Goal
Words
Speech
Recognition
Personality?
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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 ICIDS 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).
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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
DM
SLU
TTS
Text-to-
Speech
Synthesis
Spoken Language
Understanding
Dialogue
Management
ASR
SLG Spoken
Language
Generation
Data,
Rules
Words
Meaning
Speech, Nonverbals Speech, Nonverbals
Goal
Words
Speech
Recognition
Personality?
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Language Generation Module
What to say
Content
Planner
How to Say It
Sentence
Planner
Surface
Realizer
Prosody
Assigner
What is Heard
Speech
Synthesizer
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Variation controlled by the Language
Generator
What to say
Content
Planner
How to Say It
Sentence
Planner
Surface
Realizer
Prosody
Assigner
What is Heard
Speech
Synthesizer
Parametrized
Variation
• 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
Realization
INPUT: Dialog Act,
Content Pool
OUTPUT
UTTERANCE
VERBOSITY
RESTATEMENTS
CONTENT POLARITY
…
SYNTACTIC
COMPLEXITY
SELF-REFERENCE
…
CONTRAST: e.g.
however, but
JUSTIFY: e.g.
so, since
PERIOD
…
EXCLAMATION
HEDGES: e.g. kind of,
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
…
FREQUENCY OF USE
WORD LENGTH
VERB STRENGTH
Content
Planner
Pragmatic
Marker
Insertion
Lexical
Choice Aggregation
Syntactic
Template
Selection
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Example of Pragmatic Transformation
Negation insertion “X has awful food” “X doesn’t have good food”
Wok Mania class: proper noun
number: sg
have class: verb
awful class:adjective
food class: noun
number: sg
article: none
Obj Subj
ATTR
WordNet
Database Look for antonym
“good”
- Negate verb
- Replace adjective
by antonym Wok Mania class: proper noun
number: sg
have class: verb
negated: true
good class:adjective
food class: noun
number: sg
article: none
Obj Subj
ATTR
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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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7.0 6.5 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0
Extraversion rating
40
30
20
10
0
Utt
era
nc
e
co
un
t
Extravert
Introvert
1st method: Rule-Based Extraversion
Generation Use correlations in literature to set parameters
Significant perceptual differences p < .01
As binary classification, 90% accuracy
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2nd Method: Overgenerate and Rank
Generator
“WokMania is a great
place, because the
food is good, isn’t it?”
Statistical
Regression/Ranking Model Estimates scores from features,
e.g. verbosity, hedges
“Yeah, even if WokMania
is expensive, the food is
nice, I am sure you would
like WokMania!”
“Err… this restaurant’s
not as bad the others.”
…
Input
personality
score
e.g. 2.5 out of 7
Closest estimate, utterance 2:
“Err… this restaurant’s not as bad the others.”
Feature vector 1 Feature vector 3 Feature vector 2 Feature vector n
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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
Better than inter-rater correlations for random utterances (r = .40)
Correlation Abs. error (1 to 7)
Extraversion .60 .65
Emotional stability .51 .82
Agreeableness .58 .67
Conscientiousness .38 .84
Openness to experience .39 .92
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Output
Extraversion
Score
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Agreeableness score =
1.9614 +
0.919 * Content Plan Tree:Content Polarity +
0.692 * RST Tree Modification:Polarisation +
0.593 * Hedge Insertion:down_subord_it_seems_to_me_that +
0.432 * Hedge Insertion:in_group_marker=1 +
0.414 * Hedge Insertion:down_err=0 +
0.326 * Tag Question Insertion=0 +
0.314 * Hedge Insertion:down_somewhat=0 +
0.305 * Hedge Insertion:swearing=0 +
…
-0.0878 * Demonstrative Referring Expressions +
-0.0915 * Content Plan Tree:Repetitions Polarity +
-0.1942 * Content Plan Tree:Concessions +
-0.2004 * Positive Content First +
-0.2099 * Aggregation Operation Probabilities:contrast:PERIOD+
-0.3284 * Hedge Insertion:down_subord_it_seems_that +
-0.6367 * Hedge Insertion:competence_mitigation_come_on
Linear regression model for Agreeableness
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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
Correlation between target scores and average ratings
Extraversion = 3.5
Neuroticism = 1.7
Agreeableness = 6.5
Conscientiousn. = 4.0
Openness = 4.5
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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?
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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
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)
Hypoth: Persuasiveness could be increased by
expressing dominance with personality (Marwell, 1967,
Mehrabian, 1995)
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Natural Language and Dialogue Systems Lab
Evaluating the Effect of Gesture and
Language on Personality Perception in
Conversational Agents
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: o o o o o o o
2.Reserved, quiet: o o o o o o o
3.Naturalness o o o o o o o
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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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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?
Annie Hall: Getting 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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Role playing game
Solve a mystery by
talking to ‘animal
spirits’
Hypotheses: Dynamic
elements will increase
engagement &
immersion
Natural Language and Dialogue Systems http://nlds.soe.ucsc.edu
SPYFEET: Dynamic NPC/Player dialogue
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4. Generate
features
reflecting
linguistic
behaviors
Pulp Fiction
Script
Vincent’s
Dialogue
Jules’
Dialogue
Other’s
Dialogue
Jules’
Dialogue Vincent’s
Dialogue
Jules’
LIWC results Vincent’s
LIWC results
Jules’ Tag
Question
Ratio Vincent’s Tag
Question
Ratio
Jules’
other
features Vincent’s
other
features
Jules’
Overall
Polarity Vincent’s
Overall
Polarity
…
…
1. Collect
movie scripts
from IMSDb
2. Extract
utterances for
each character
3. Select
leading roles
(dialogue > 60
turns)
Method
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Jules’
Learned
Model
Vincent’s
Learned
Model
Generated
features Vincent in
SpyFeet
utterances
PERSONAGE
generator
(ENLG
engine)
Jules’ in
SpyFeet
utterances
Others in
SpyFeet
utterances
…
5. Learn models
of character (z-
scores)
6. Generate new
utterances using learned
models to control
parameters of our dialogue
generator
Story domain:
SpyFeet
utterances
Method (cont)
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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
z-score >1 or <-1 is more than one standard deviation
away from the average
Indicates parameters that should be high or low
Annie’s
z-score
Annie’s vector Averaged female population
Standard deviation female population
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Example: Model Learned for Annie
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-first-
ratio 1.00
… etc.
Map character model to PERSONAGE parameters: weighted average of
features.
Parameters either binary, or scalar range 0…1.
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Annie (Annie Hall)
original dialogue sample
• H’m?
That’s, uh … that’s pretty serious stuff there.
Yeah? Yeah?
M’hm? M’hm.
Yeah.
U-huh. • Hi. Hi, hi.
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
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?
Original and Generated Utterances
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.
Learning
Linguistic
Features
Generation
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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
*significant differences between character and character models of
each row
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
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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
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Come Visit !! University of California Santa
Cruz
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Alfred Experiment: Gaze & Personality
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Mary: Gesture & Personality