Paper 2

EFFECTS OF SELF-DISCLOSURE
AND EMPATHYIN HUMAN-COMPUTER DIALOGUE
R. Higashinaka, K. Dohsaka, and H. Isozaki
NTT Communication Science Laboratories,NTT Corporation

1. Summary• We want to reveal the effects of system selfdisclosure and empathy on user’s felt closeness andsatisfaction in human-computer dialogue• By statistically analyzing dialogues between users
and systems, we found the following:
9 Empathic utterances of users strongly indicate
increasing user’s felt closeness and satisfaction
9 System’s empathic utterances are effective forinducing empathic utterances from users
9 Self-disclosure of users increase when they havepositive preferences on topics being discussed

2. Problem and Approach• Self-disclosure and empathy are known to play animportant role in social dialogues• However, it is not known how system’s individual
linguistic acts influence dialogue qualities• We collect dialogues in which users converse withsystems with different occurrence distributions ofself-disclosure and empathic utterances• We analyze correlation between the counts ofdialogue acts related to self-disclosure/empathy
and user’s felt closeness/satisfaction

3. Dialogue System• Japanese keyboard-based dialogue system• Domain: likes and dislikes about animals• System and user can make self-disclosure /agreement (empathy) / disagreement (antipathy)• System can change the occurrence probabilities
of its self-disclosure / agreement / disagreement• System’s understanding grammar was realized asa WFST (2,276 words)• Hand-crafted templates and rules for dialogue
management and surface generation

4. Dialogue Sample
Utterance

Dialogue act

System HelloGreeting

User

Greeting

Hello

System Lets’ discuss likes and dislikes
about animals.

Open dialogue

User

Do you like insects?

Question

System Do you like insects?

Question

User

Self-disclosure (preference)

I like fireflies.

System You like fireflies.
User

Agreement (empathy byrepetitions)

I like fireflies because they glow. Self-disclosure (reason)

System I also like them very much.

Agreement (empathy)

User

QuestionWhy do you like fireflies?

System I like them too.
They are evanescent.

Agreement (empathy)
Self-disclosure (reason)5. Data Collection and Analysis• Users: 50 Japanese adults (25 males/females)• System with 18 different occurrence distributions ofself-disclosure / agreement / disagreement• Each user talked to the system with 18 occurrence
distribution settings (total: 900 dialogues)• After each dialogue, users filled out a questionnaire
for subjective evaluation of the dialogue• We annotated user’s correct dialogue acts
• We analyzed correlation between the counts of
dialogue acts and user’s felt closeness/satisfaction

6. Indicator of User’s Felt
Closeness and Satisfaction• Among various types of dialogue acts,
user agree count is relatively more correlatedwith both user’s felt closeness and satisfaction• User agree count (count of dialogue acts related
to the user’s empathy) is an strong indicator ofincreasing user’s felt closeness and satisfaction
Closeness and satisfaction
in dialogues with more than one
user agree count>

Closeness and satisfaction
in dialogues withs zero or one
user agree count

ANOVA: F = 20, p < 0.0001; Multiple comparison test: p < 0.002

7. How to Increase User Agree Count• We need to increase user agree count for betteruser’s felt closeness and satisfaction• Multiple linear regression analysis
objective variable: user agree counts
explaining variables: counts related to system acts
System agreement (performed independently of userutterances) is effective for inducing agreement from users
Regression coefficients

System disclosure count

0.393

System agree count

0.245

System acknowledge count

0.189

System question count-0.172

8. How to Increase User
Self-disclosure• Inducing self-disclosure is important formarketing/recommendation purposes• Multiple linear regression analysis
objective variable: user self-disclosure countexplaining variables: counts of animals thatuser/system likes or dislikes
User’s self-disclosure tends to increase when users
have a positive preference for topics being discussed
Regression coefficientsUser preference positive count0.496

User preference negative count0.358