EFFECTS OF SELF-DISCLOSURE AND EMPATHY IN HUMAN-COMPUTER DIALOGUE
Ryuichiro Higashinaka, Kohji Dohsaka, and Hideki Isozaki
NTT Communication Science Laboratories, NTT Corporation{rh,dohsaka,isozaki}@cslab.kecl.ntt.co.jp
ABSTRACT
To build trust or cultivate long-term relationships with users, conversational systems need to perform social dialogue. To date, research
has primarily focused on the overall effect of social dialogue inhuman-computer interaction, leading to little work on the effects ofindividual linguistic phenomena within social dialogue. This paper
investigates such individual effects through dialogue experiments.
Focusing on self-disclosure and empathic utterances (agreement anddisagreement), we empirically calculate their contributions to the dialogue quality. Our analysis shows that (1) empathic utterances byusers are strong indicators of increasing closeness and user satisfaction, (2) the system’s empathic utterances are effective for inducing
empathy from users, and (3) self-disclosure by users increases whenusers have positive preferences on topics being discussed.

S1
U1
S2
U2
S3
U3
S4
U4
S5
U5
S6
U6
S7

Index Terms— Natural language interfaces, Interactive systems
1. INTRODUCTION
Conversational systems need to build trust or cultivate long-term relationships with users through social dialogue when they want users
to engage in possible face-threatening tasks such as real-estate transactions [1] or to disclose personal information for personalized services such as health-care [2]. Social dialogue includes greetings,
chatting about general topics like the weather, exchanging personal
preferences, and so forth. Since social dialogue comprises various
linguistic phenomena, systems must be able to make an appropriate linguistic choice to have the desired effect. However, because
most research has been primarily concerned with the effect of social
dialogue as a whole in human-computer interaction, little work has
sought to qualitatively examine the individual effects of linguistic
phenomena within social dialogue.
This paper investigates the individual effects of linguistic phenomena through dialogue experiments. Focusing on self-disclosureand empathy (agreement and disagreement), we collected dialogue
data between a system and users to calculate the correlations between the counts of self-disclosures or empathic utterances and the
dialogue quality perceived by users. We then analyzed the correlations to reveal the individual effects of self-disclosure and empathy.
Figure 1 shows an excerpt of a dialogue in our experiments. In the
dialogue, the user and system discuss likes and dislikes about animals (fireflies in this case), disclosing preferences and agreeing witheach other. Since dialogue participants can choose to self-disclose or
to be empathic at any moment, it is important for a system to know
the individual effects of its actions to improve dialogue quality.
Section 2 describes our approach in detail. Section 3 describes
our dialogue system and the dialogue data collection. Section 4shows the results of the analysis and describes our main findings.
Section 5 summarizes and mentions future work.

U7

Hello (GREETING )Hello (GREETING )Let’s discuss likes and dislikes about animals.
(OPEN - DIALOGUE)
Do you like insects? (Q - DISC - P )Do you like insects? (Q - DISC - P )I like fireflies. (DISC - P )You like fireflies. (REPEAT)
I like fireflies because they glow. (DISC - P - R)
I also like them very much. (DISC - AGREE - P )Why do you like fireflies? (Q - DISR - R)
I like them too. (DISC - AGREE - P )They are evanescent. (DISC - R)
I like them because they are beautiful. (DISC - P - R)
I see (ACK ), I understand. (EMP )I also think they are beautiful. (DISC - AGREE - R)
Can you tell me more about why you like fireflies?
(Q - DISC - R - OTHER)
I like them because they glow at night. (DISC - P - R)

Fig. 1. An excerpt of a dialogue in our experiment. S stands for system utterance and U for user utterance in the dialogue. Dialogue acts
corresponding to utterances are shown in parentheses (See Section
3.1.2 for their meanings). The dialogue was originally in Japaneseand was translated by the authors.
2. APPROACH
As linguistic phenomena in social dialogue, we focus on selfdisclosure and empathy, both of which are known to contribute
greatly to the closeness between conversational participants [3, 4, 5].
In addition to closeness, we are also concerned with user satisfactionof a dialogue because it is commonly used to evaluate conversational
systems. Our objective is to reveal their individual effects on thecloseness and user satisfaction in a qualitative manner. Our approachfor this is to collect as many dialogues as possible and calculate thecorrelations between the counts of self-disclosure and empathy andthe closeness or user satisfaction evaluated from questionnaires.
Two specific aims are to determine what linguistic behavior mostaffects closeness and user satisfaction, and to clarify how dialoguesystems should make its linguistic choice in order to increase them.
The former will help us derive appropriate dialogue strategies, andtaken together, these findings will help us achieve our eventual goalof creating systems that can achieve better closeness and user satisfaction. We are also interested in how systems can be made to encourage self-disclosure by users because one of the important goalsof dialogue systems is to have them elicit user information for personalization or marketing purposes.
To collect dialogues, we built an automated dialogue system using text input. We did not take a Wizard-of-Oz approach because weare simply interested in the correlation between the observed linguistic phenomena in a human-computer dialogue and the user’s perception of that dialogue. Unless automated dialogues end up in complete failure, systems do not need to have human-level dialogue processing capability. In addition, we are also interested in collecting
realistic dialogue data with the current level of dialogue technology.
Currently, the system works only on text input in order to avoid theinfluence of speech recognition errors. We aim to deal with speech
input in the future.
The selection of the dialogue domain is important because thedomain has to be one in which users can easily self-disclose or show
empathy. Further, the content to be discussed should not be too complex in order for our system to work sufficiently. For these reasons,we decided to develop our system in the animal domain where conversational participants talk about their likes and dislikes regarding
animals. Our reasoning here is that everyone knows at least someanimals and have preferences for them.
3. EXPERIMENT
We first implemented a dialogue system in the animal domain andthen collected dialogues using human subjects. Dialogue data annotation was performed to find exact counts of self-disclosure andempathy.
3.1. System
The system is a Japanese keyboard-based dialogue system and has achat-like interface. It has some manually prepared knowledge of animals and preferences and can discuss likes and dislikes about themwith users. A dialogue starts with the system’s greeting. The systemthen prompts the user to input an utterance. The user marks the endof an utterance by pressing Enter. Turns are strictly controlled to alternate. A dialogue ends when one of the conversational participants
explicitly declares the end of the dialogue.
When a user enters an utterance, it is first parsed by the utterance understanding component into a meaning representation calleda dialogue act. A dialogue act typically comprises (i) a dialogue act
type that identifies the main intent of the user’s utterance and (ii) its
auxiliary information encoded as attribute-value pairs. The dialogue
act is then passed onto the dialogue manager, which decides the next
action to take considering both the dialogue act and the dialogue history. Finally, the utterance generation component receives the next
action of the dialogue manager to generate the system’s utterance. In
what follows, we describe how the knowledge of animals and preferences were prepared, the dialogue acts, and how each component
works.

system not being able to say anything due to its indifference. Reasons are randomly selected from the animals’ attributes with one exception; some obvious positive attributes (e.g., beautiful) are not selected as reasons to have a negative polarity and likewise for several
negative attributes. In the case of fireflies, kirei and utsukushii canonly be reasons for the system to like fireflies. chiisai and hakanai
can be reasons for both polarities. No reason is given to animals forwhich the system has the neutral polarity.
3.1.2. Dialogue Acts
The system recognizes 22 dialogue acts, which fall into one of six
categories: self-disclosure, agreement (empathy), disagreement (opposite of empathy; antipathy), dialogue-control, question, and response. We list the dialogue acts in each category as shown below.
Self-disclosure: We have DISC - P , DISC - R, DISC - P - R, DISC - R OTHER and RES . DISC - P is used to disclose a proposition P . A
proposition is either like(X,Y) or dislike(X,Y), meaning thata conversational participant X likes an animal Y or vise versa.
DISC - R discloses a reason R for some aforementioned proposition P . DISC - P - R discloses P and R at the same time, and
DISC - R - OTHER discloses R in addition to already mentioned
reasons (e.g., “another reason is that. . . ”). We consider RES ,which is a response to a yes-no question (i.e., Q - DISC - P ), tobe self-disclosure.
Agreement: We have DISC - AGREE - P , DISC - AGREE - R, EMP , and
REPEAT. DISC - AGREE - P and DISC - AGREE - R show agreement to the propositions or reasons mentioned by the partner. EMP denotes an explicit empathic action (e.g., “I understand”), and REPEAT means the repetition of the partner’s
previous self-disclosure to show understanding.
Disagreement: We have DISC - DISAGREE - P and DISC - DISAGREE R . They show disagreement to the propositions or reasonsmentioned by the partner; e.g., saying “I don’t like cats” tothe partner who has already disclosed that he/she likes cats.
Dialogue-control: We have GREETING , GOODBYE, OPEN DIALOGUE, and Q - OPEN - DIALOGUE, CLOSE - DIALOGUE as
dialogue-initiating/ending acts. We also have SHIFT- TOPIC,
which introduces a new topic (animal) into the dialogue.
Question: We have four questioning acts, Q - DISC - P , Q - DISC - P OPEN (an open question such as “how about cats?”), Q - DISC R , and Q - DISC - R - OTHER , that ask for propositions or reasonsof the partner.
Acknowledgment: We have ACK , which acknowledges the partner’s utterance using back-channels.
3.1.3. Utterance Understanding

3.1.1. Knowledge of Animals and Preferences
The system can recognize 90 animals and has a list of attributes (adjectives) for them. The attributes are used to state reasons for preferences. The animals and their attributes were taken from an Associative Concept Dictionary [6], which lists various nouns with theirassociative words. We extracted entries of animals in this dictionary
together with adjectives in their associative words. For example, hotaru (fireflies) has four attributes; kirei (beautiful), chiisai (small),utsukushii (beautiful), hakanai (evanescent). The system in Fig. 1
uses this list.
The system’s preferences for animals are decided randomly under a probability distribution: positive, 45%; negative, 45%; neutral,
10%. We limited the occurrence of the neutral polarity for fear of theUser utterances are first separated into word tokens using a Japanesemorphological analyzer (n.b., there is no explicit word boundary inJapanese) and are then converted into dialogue acts using an understanding grammar realized as a weighted finite state transducer(WFST) in a manner similar to [7]. We defined sequences of words
that form dialogue acts and from them compiled a WFST that maps
a sequence of words into a scored list of dialogue acts augmented
with attribute-value pairs. For example, “I like fireflies” would beparsed into (DISC - P polarity=‘+’ animal=‘fireflies’) and “I like fireflies because they are evanescent” into (DISC - R polarity=‘+’ animal=‘fireflies’ reason=‘evanescent’). In all, our grammar has a vocabulary of 2,276 words, including 1,005 adjectives taken from theevaluative expression dictionary [8].

Table 1. Questionnaire results averaged over all dialogues (900 dialogues). Values in parentheses show the mean of the standard deviations within one participant.
Q1
Q2
Q3
Q4
Q5
Q6
Q7

Questionnaire item
Quality of system utterance
System understanding quality
Smoothness of dialogue
Closeness perceived by user
Closeness shown by system
User satisfaction
Willingness for future use

Avg.
3.73
2.71
2.70
2.58
2.67
2.52
2.46

SD.
1.19 (0.65)
1.24 (0.89)
1.25 (0.83)
1.21 (0.83)
1.16 (0.79)
1.21 (0.79)
1.25 (0.72)

3.3. Data Annotation
We annotated user utterances with correct dialogue acts. A single
annotator, who was not one of the authors, annotated each dialogue.
Since users often made utterances that were not defined in our dialogue act set, we newly introduced DISC - OTHER and OTHER, whichwere annotated for self-disclosures not related to the animal domain
and for out-of-domain utterances, respectively. The system’s dialogue act type recognition accuracy (excluding DISC - OTHER andOTHER ) was 50.03%.
4. ANALYSISThe system uses four flags to keep track of the dialogue: SELF - DISC P , SELF - DISC - R , PARTNER - DISC - P , and PARTNER - DISC - R . They
take either true or false and keep a record of whether the system has
disclosed a proposition about the animal in question, whether thesystem has disclosed a reason, and likewise for the conversational
partner. In addition to these flags, the system maintains the name of
the animal being discussed and the lists of animals introduced so far
in a dialogue. Reasons expressed by the user are also stored as a list
so that they can be referred to later to make agreement/disagreement.
Hand-crafted rules process the user’s dialogue acts to set the flags,
and the dialogue manager decides the next action based on them. For
each combination of flags, the system has a predefined list of action
sequences and randomly selects one as its next action. This random
selection procedure is affected by three 0–1 probabilistic parametersthat increase/decrease self-disclosure, agreement, and disagreement,
respectively.

We used 900 dialogues (excluding test dialogues) for our analysis.
For each dialogue, we counted the number of dialogue acts in orderto derive 19 counts. Based on the dialogue act category, we havethe (1) user disclosure count, (2) user agree count, (3) user disagree
count, (4) user question count, (5) user acknowledge count. We also
have the (6) user other count for counting OTHER. DISC - OTHER is
included in (1). We ignore dialogue-controlling acts. We derived(7)–(11) as counterparts of (1)–(5) for system dialogue acts.
Users are likely to prefer systems that show similarity to users[9]. In addition, users may simply like dialogues in which animals
that they like appear. To investigate this issue, we have the (12) preference agree count and (13) preference disagree count, corresponding to the number of animals on which the user and the system agreed
on preferences. We also have the (14) user preference positive count,(15) user preference negative count, and (16) user preference neutral
count, representing the number of animals for which users showed
positive, negative, and neutral preferences, respectively. The counterparts for these for the system were also counted (17–19).

3.1.5. Utterance Generation

4.1. Closeness and User Satisfaction

We prepared possible surface realizations for each dialogue act. The
system makes its utterance by randomly selecting one of the realizations. For example, we have “I [don’t] like OBJ because it is EXP ”
as a realization for DISC - P - R. At runtime, OBJ is replaced with theanimal name in question, [don’t] is added when the system’s polarity for the animal is negative, and EXP is replaced with one of thereasons.

We calculated the correlation of the 19 counts with closeness (Q4)and user satisfaction (Q6). Tables 2 and 3 show the top-five counts
correlated with closeness and user satisfaction, respectively. Here,
we used Spearman’s rank correlation coefficient for correlations.
It can be seen that the user agree count is relatively more correlated with both closeness and user satisfaction than other counts,
leading us to believe that we should focus on increasing this count for
better dialogue. An analysis of variance (ANOVA) also revealed that
dialogues with zero, one, or more than one user agree count are significantly different in closeness (F(2,897)=20.1, p<0.0001) and user
satisfaction (F(2,897)=22.0, p<0.0001), and a non-parametric multiple comparison test (Steel-Dwass test) showed that dialogues withmore than one user agree count are significantly better in closeness(p<0.0002) and user satisfaction (p<0.002) than those with zero orjust one user agree count.
It is surprising that the user’s self-disclosure shows little correlation with either closeness (R=0.029, p=0.39) or user satisfaction(R=0.065, p=0.052). This means that the amount of user’s selfdisclosure does not necessarily mean they are willing to talk; perhaps
self-disclosure is costly for users.

3.1.4. Dialogue Manager

3.2. Data Collection
We recruited 50 Japanese adults (25 males and 25 females) for datacollection. They were paid for their participation. The duration ofeach dialogue was limited to four minutes. The participants were
notified to end the dialogue when the limit approached. We prepared 18 parameter patterns to make variations in the system’s selfdisclosure, agreement, and disagreement. Each participant talked tothe system with a randomly selected parameter pattern, resulting in
18 dialogues. Including two test dialogues in the beginning, we collected 20 dialogues from each participant for a total of 1,000 (50 ×20) dialogues.
After each dialogue, the participants filled out a questionnaire(five-point Likert scale) that asked for their subjective evaluation of
the dialogue. Table 1 summarizes the results. From the averaged
scores, relatively low system performance can be seen. However,what is important is the standard deviation for Q4 (closeness) andQ6 (user satisfaction). The system succeeded in collecting dialoguesof varying closeness and user satisfaction, which is necessary for ouranalysis.

4.2. Increasing User Agree CountWe need to increase the user agree count for better dialogue. Although the user agree count can be increased when the system knows
what the user is likely to agree, it is difficult to achieve unless wehave some prior knowledge of user preferences. Therefore, we investigate system actions that may increase the user agree count. We

Table 2. Top-five counts correlated with closeness. All counts show
significant correlations (p<0.0001).
Correlation coefficient
1 User agree count
0.196
2 System agree count
0.146
3 User preference positive count
0.144
4 User other count−0.1275 User preference negative count−0.113

Table 4. Results of the multiple linear regression analysis with theuser agree count as the objective variable and the counts related tosystem actions as explaining variables (Adjusted R2 =0.245).
Regression coefficient
System disclosure count
0.393
System agree count
0.245
System acknowledge count
0.189
system question count−0.172

Table 3. Top-five counts correlated with user satisfaction. All counts
show significant correlations (p<0.0001).
Correlation coefficient
1 User agree count
0.202
2 System disclosure count
0.153
3 System agree count
0.152
4 User preference positive count
0.121
5 User preference neutral count
0.115

Table 5. Results of the multiple linear regression analysis with theuser disclosure count as the objective variable and the counts related to user/system preferences as explaining variables (Adjusted
R2 =0.253).
Regression coefficient
User preference positive count
0.496
User preference negative count
0.358

performed a multiple linear regression analysis with the user agree
count as the objective variable and the counts related to system actions as explaining variables. By analyzing the regression model, we
can obtain the relative importance of system actions in relation to theuser agree count. We do not simply list correlation coefficients here
because we specifically want to find out the relative importance ofsystem actions.
Table 4 shows the results of the multiple linear regression analysis. Explaining variables that caused multi-collinearity were removed in the analysis. From the table, we can see an obvious result that the system disclosure count has a high standard partial regression coefficient; users cannot agree unless the system discloses
something. What we think is most interesting is that the system agree
count has a relatively high regression coefficient. As the system wasbuilt so that it would not be affected by whether users agree or disagree with it, we can safely say that users tend to agree with systems
that agree with users. It seems to be a good strategy for the system
to agree with users as much as possible.

ysis of the collected dialogue data revealed that (1) increasing user
agreement is the key to achieving better closeness and user satisfaction, (2) the system’s agreement is effective for inducing agreementfrom users, and (3) self-disclosure by users increases when users
have positive preferences on topics being discussed. These results
provide useful insight into how dialogue systems should behave insocial dialogue. As future work, we plan to verify our findings ina more controlled dialogue experiment. Sequences of dialogue acts
rather than their number need to be considered for further analysis.
We also plan to improve the system’s understanding and generation
ability as well as its coverage of topics and domains.

4.3. Increasing User Disclosure Count
There are cases where we want to increase the user disclosure count,such as when we want to elicit information from the user. Although
it may be possible to perform a multiple linear regression analysis tofind system actions that drives users to disclose, we cannot do it because the user’s self-disclosure affects the system’s choice of actions
considerably, which makes the analysis of the causal relationshipdifficult. Therefore, we performed a multiple linear regression analysis with the user disclosure count as the objective variable and thecounts related to preferences as explaining variables.
Table 5 shows the results, with only two explaining variables
because others were removed due to multi-collinearity. From thetable, we can say that users tend to self-disclose when they talk about
topics they like. They also self-disclose a fair amount on things they
do not like. It is surprising that the preference agree count does notappear in the results. Considering that the user preference negative
count has a negative correlation with closeness (Table 2), the best
strategy to increase the user’s self-disclosure is to bring up topics ofinterest to the user as much as possible.
5. SUMMARY
This paper investigated the individual effects of self-disclosure andempathic utterances in human-computer social dialogue. Our anal-

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