Towards Developing Dialogue Systems with Entertaining Conversations

Hai-Long Trieu, Hiroyuki Iida, Nhien Pham Hoang Bao and Le-Minh Nguyen

Japan Advanced Institute of Science and Technology, Nomi City, Ishikawa, Japan

Keywords:

Dialogue Systems, Game Reﬁnement, Goal-driven Dialogue Systems, Entertaining Conversation.

Abstract:

This paper explores a novel approach to developing a dialogue system that is able to make entertaining conver-

sations with users. It proposes a method to improve the current goal-driven dialogue systems which support

users for speciﬁc tasks while satisfying users’ goals with entertaining conversations. It then develops a dia-

logue system in which a set of features are considered to generate entertaining conversations, while reasonably

prolonging the original too short dialogue. The game reﬁnement measure is employed for the assessment of

attractiveness since the conversations in dialogue systems can be seen as the process by which a player creates

shoots or moves to win a game. The dialogues generated by the proposed method are evaluated by human

subjects. The results conﬁrm the effectiveness of the proposed method. The present idea can be a promising

way to realize dialogue systems with entertaining conversations although further investigations are needed.

1 INTRODUCTION

Dialogue systems have become applicable in various

tasks including technical support services, language

learning tools, and entertainment (Young et al., 2013;

Shawar and Atwell, 2007). Recently, the systems

have been popular in different domains (Banchs and

Li, 2012) and commercially exploitable (Pieraccini

et al., 2009; Griol et al., 2010). In dialogue systems, a

system (or robot) communicates with a human using

natural languages in spoken-based or text-based con-

versations. Dialogue systems can be divided into two

branches: task-oriented (or goal-driven), and chat-

oriented (or non-goal-driven). Goal-driven systems

focus on supporting users to complete a speciﬁc task

like accommodation services, booking transportation

or requesting speciﬁc information (Busemann et al.,

1997; Seneff and Polifroni, 2000; Stallard, 2000).

Meanwhile, the objective of non-goal-driven systems

is for entertainment or to provide a means for partic-

ipating in a game (Weizenbaum, 1966; Ogura et al.,

2003; Wallis, 2010).

The most successful approach for goal-driven sys-

tems is based on the partially observable Markov

decision process (POMDP) (Young et al., 2013;

Pietquin and Hastie, 2013) and learning features using

neural network models (Henderson et al., 2014). For

non-goal-driven systems (Ritter et al., 2011; Banchs

and Li, 2012; Ameixa et al., 2014), there are ap-

proaches like using neural networks in natural lan-

guage modeling and machine translation (Sordoni

et al., 2015; Shang et al., 2015).

For the goal-driven applications, one of the main

tasks of current dialogue systems is to satisfy users’

goals. This is an important task to make the systems

applicable. Nevertheless, this may result in the prob-

lem that the systems tend to directly respond to satisfy

users’ goals immediately; in other words, the dialogue

speed is too fast. The systems will be successful and

effective when they not only satisfy users’ goals but

also generate interesting and intelligent conversations

during the dialogues’ progress to attract the users.

In this work, we improve goal-driven dialogue

systems to generate interesting and intelligent conver-

sations while beneﬁting from game reﬁnement theory

(Iida et al., 2004). The idea originates from the corre-

spondences between dialogues and games including

goals and progress to obtain the goals. In order to

win a game, players try to create successful shoots

(e.g. in soccer) or moves (e.g. in chess). Meanwhile,

for dialogues, users ask questions to a dialogue sys-

tem in order to reach speciﬁc goals, and the conver-

sations between the user and the system to obtain the

user’s goals correspond to shoots or moves in games.

Therefore, the progress to satisfy the user’s goals in

dialogues can be seen as the progress when players

enjoy the game. In order to generate interesting dia-

logues, we apply game reﬁnement theory (Iida et al.,

2004; Sutiono et al., 2015), which has been proposed

as a measure of game sophistication. The dialogues

are generated and their length is reasonably controlled

with a focus on a set of important features of conver-

Trieu H., Iida H., Pham Hoang Bao N. and Nguyen L.

Towards Developing Dialogue Systems with Entertaining Conversations.

DOI: 10.5220/0006192105110518

In Proceedings of the 9th International Conference on Agents and Artiﬁcial Intelligence (ICAART 2017), pages 511-518

ISBN: 978-989-758-220-2

511

sations. Experiments are conducted using a data set

of dialogues about restaurant information. The game

reﬁnement measure is used to assess the of attractive-

ness of dialogues. In addition, we conduct a web-

based human evaluation to score the generated dia-

logues.

The structure of the paper is as follows. In Sec-

tion 2 we introduce our proposed method to improve

dialogue systems. Speciﬁcally, the correspondences

between dialogues and games are analyzed before we

discuss the game reﬁnement measure, the means that

we use to improve dialogue systems. Then, we de-

scribe proposed features to generate dialogues. Ex-

periments, results and the discussion are presented in

Section 3. Finally, concluding remarks are drawn in

Section 4.

2 DEVELOPING DIALOGUE

SYSTEMS

In this section, we analyze the correspondences be-

tween dialogues and games. Then, we discuss game

reﬁnement theory, by which we improve dialogue sys-

tems that generate attractive dialogues. The dialogues

are generated using some features that are described

thereafter.

2.1 Dialogues and Games

Dialogues contain some characteristics that are also

included in games. In order to win a game like chess

or soccer, players should have successful moves or

shoots. In dialogue systems, the user also has some

speciﬁc goals, and each informative answer generated

by the system that satisﬁes the user’s goal can be seen

as successful moves or shoots in games. The progress

of conversations between the system and the user cor-

responds to the progress in games, in which the user

asks questions to reach the goals. When the user ob-

tains all of the goals from information generated by

the system, the dialogue can be complete, or in other

words, the game ﬁnishes. In this way, when the user

has a speciﬁc number of goals in advance, the dia-

logues can be seen as pre-set score games like volley-

ball or tennis.

Game players usually feel excited with games not

only because they win the game but because the game

progress is also interesting. The question is how to

improve dialogue systems so that they not only gener-

ate informative answers to satisfy users’ goals but also

help users feel interested in the dialogues’ progress?

2.2 Game Reﬁnement Measure

The game reﬁnement measure was derived from a

mathematical model of game outcome uncertainty

within the framework of game reﬁnement theory (Iida

et al., 2004). In this work, dialogues can be seen as

a kind of game, and the game reﬁnement measure is

used to evaluate and improve dialogue systems.

2.2.1 Model

In the game reﬁnement theory, there are two impor-

tant factors: the game speed and the game information

progress (Sutiono et al., 2015). Game speed relates to

the scoring rate, while game information progress fo-

cuses on game outcome. In well-known games like

soccer and basketball, the scoring rate is calculated

based on two factors: the total score (goals) and the

time or steps to achieve the goal(s). The total score is

the average number of successful shoots (say G) while

the steps needed to achieve the goal are based on the

average number of attempted shoots (say T ). There-

fore, the scoring rate or game speed of the games can

be calculated by the ratio of G to T . Meanwhile,

other sport games in which the goal is set in advance,

like volleyball and tennis, the average number of to-

tal points per game corresponds to the time or steps

to achieve the goal. For boardgames such as Go and

chess, the steps are based on the average depth of

game tree (game length), and the goal is estimated by

the average branching factor.

When one already knows the game outcome, for

instance when the game ﬁnishes, the game progress

x(t) will be given as a linear function of time t with

0 ≤t ≤T and 0 ≤x(t) ≤G, as shown in Equation (1).

x(t) =

t (1)

Nevertheless, it is difﬁcult to know the game infor-

mation progress x(t) during the game until it ﬁnishes.

In other words, the game outcome is uncertain by the

endgame in many games, called balanced games or

seesaw games. We show in Figure 1 an illustration

of game progress in terms of outcome certainty. Fig-

ure 1 shows that the game outcome is uncertain until

the very end of the game in balanced games or seesaw

games. This leads to an exponential function as a re-

alistic model of game information progress, which is

given by Equation (2).

x(t) = G(

)

(2)

Here n stands for a constant parameter which is given

based on the perspective of an observer in the game

considered. If one knows the game outcome, for ex-

ample after the game, or if one can exactly predict

ICAART 2017 - 9th International Conference on Agents and Artiﬁcial Intelligence

512

in advance the game outcome and its progress, then

we have n = 1, where x(t) is a linear function of time

t. During the in-game period, various values of the

parameter n for different observers, including players

and supporters, are determined. For example, some

observers might be optimistic with 0 ≤ t < 1. How-

ever, when one feels any difﬁculty to win or achieve

the goal, the parameter would be n > 1. Meanwhile,

we reasonable assume that the parameter would be

n ≥ 2 in many cases, like balanced or seesaw games.

Thus, the acceleration of game information progress

¯x(t) is obtained by deriving Equation (2) twice. Solv-

ing it at t = T , we have Equation (3).

¯x(T ) =

n(n −1) (3)

It is assumed in the current model that the game

information progress in any type of games is happen-

ing in our minds. Hence, it is reasonably expected that

the larger the value

is, the more the game becomes

exciting due to the uncertainty of the game outcome.

Thus, its root square

√

is used as a game reﬁnement

measure (say R) for the game under consideration, as

deﬁned in Equation (4).

R =

√

(4)

Figure 1: A game progress model of outcome certainty.

2.2.2 Reﬁnement Values of Sophisticated Games

We show, in Table 1, several sophisticated games in-

cluding soccer, basketball and table tennis from sports

games, chess and Go from board games (Sutiono

et al., 2015). Note that G and T of board games cor-

responds to the branching factor and the game length,

respectively. We see that sophisticated games have a

similar R value which we call a zone value between

0.07 and 0.08. This indicates the same or similar de-

gree of game sophistication where players may feel

the same degree of engagement or excitement regard-

less of different type of games.

Table 1: Reﬁnement values of some well-known games.

Games G T R

Soccer 2.64 22 0.073

Basketball 36.38 82.01 0.073

Table Tennis 54.863 96.465 0.077

Chess 35 80 0.074

Go 250 208 0.076

Figure 2: An example of the greeting feature.

2.3 Generating Dialogues

The current dialogue systems focus on how to maxi-

mize the users’ goals. This is an important task that

beneﬁts for users in many applications. Nevertheless,

the further objective of dialogue systems is not only to

satisfy user’s goals but also to help the users enjoyable

with the dialogue progress. Focusing solely on users’

goals as in the current dialogue systems may result

in obtaining the goals (the dialogue’s speed) fast. In

other words, the systems tend to provide informative

answers to satisfy the user’s goals immediately. This

is useful and reasonable; nonetheless, from the view-

point of game reﬁnement theory or entertaining con-

versations, the dialogues should contain uncertainty

with a longer progress to satisfy the user’s goals grad-

ually, which enables to create intelligent dialogues.

For this purpose, we focus on some important features

such as greeting, user’s topics, dialogue prolongment,

feedback, surprising and grammar.

Greeting. The system can start conversations with

users by some random topics like weather, sports, or

foods, etc. We name this feature as greeting. Instead

of starting the progress to obtain the user’s goal im-

mediately, using random topics would help to reduce

the dialogue speed, and this is also a natural method

to start conversations of human. We present an exam-

Towards Developing Dialogue Systems with Entertaining Conversations

513

Figure 3: An example of the user’ topics feature.

ple of this feature in Figure 2, in which Figure 2(a) is

a baseline dialogue while Figure 2(b) is the dialogue

generated by using the proposed feature.

Users’ Topics. Conversations can be also started by

some topics related to the users like jobs or hobbies.

In order to do that, the system should contain a corpus

covering vocabulary and context of various domains

including jobs and hobbies. This feature can help to

reduce the dialogue speed and attract users because

this indicates that the system seems to understand spe-

ciﬁc ﬁelds or domains like human. Figure 3 presents

an example of this feature.

Dialogue Prolongment. The system sometimes

provides several information together for the user at

the same time to answer a speciﬁc question of the

user. Nevertheless, such multiple information can be

divided into smaller parts in order that the necessary

information will be provided for the user step by step.

This may attract the user raising more questions to

obtain the goals when the system partly provides in-

formation for the user gradually. By this way, the di-

alogue speed is also reduced. Nevertheless, it should

be further considered when asking so many questions

to obtain a goal may lead to negative effects for the

user. We show, in Figure 4, an example of this fea-

ture.

Figure 4: An example of the dialogue prolongment feature.

Figure 5: An example of the feedback feature.

Feedback. When the user asks a question, the sys-

tem does not need to respond directly to the question;

ICAART 2017 - 9th International Conference on Agents and Artiﬁcial Intelligence

514

Figure 6: An example of the surprising feature.

instead the system may provide some speciﬁc options

related to the answer. This helps to reduce the dia-

logue speed while the user still obtains necessary in-

formation. Figure 5 shows an example of this case.

Surprising. In the game’s progress model as shown

in Figure 1, the amount of game outcome informa-

tion in the game’s opening is small; however, this be-

comes much more as the game approaches the end.

The acceleration of game information progress fol-

lows an exponential function. We propose using a

feature namely ”surprising” in which the system tries

to ask questions related to a speciﬁc goal. In this case,

it is unusual when the system actively asks questions

as suggestions for the user even when the user has not

yet provided the request. This would enable to create

interesting conversations especially when the sugges-

tion can match with the goal in the user’s mind, which

may lead to a surprising emotion for the user. It may

be exciting when the system actively can satisfy the

user rather than the process of asking by the user and

answering by the system. This feature is reasonable

in the game reﬁnement model when suggesting op-

tions by the system may lead to a small amount of

necessary information at the beginning of the conver-

sation; however, by the end the user may obtain a goal

surprisingly when one of the options matched with a

speciﬁc goal of the user. We present an example in

Figure 6.

Figure 7: An example of the grammar feature.

Grammar. We describe here a feature which may

help to generate natural dialogues. The human-

human dialogues usually contain less formal gram-

mar. Therefore, we try to apply this feature in gener-

ating dialogues when we remove the complete gram-

mar in some cases. Figure 7 illustrates an example

of this feature. In the example, some information like

phone number or address may not need to be provided

grammatically.

3 ASSESSMENT

Our work is to improve dialogue systems which

can ensure providing informative responses to satisfy

users’ goals and create an interesting progress in di-

alogues. For this purpose, it is assumed that the ﬁrst

task of satisfying the goals is already obtained in or-

der that we can focus mainly on the second task which

produces interesting progress of dialogues. Thus, we

use an existed set of dialogues as the baseline, and

our objective is to improve the baseline dialogues that

become interesting and intelligent while retaining the

user’s goals in the dialogues.

3.1 Data

We use a data set of real dialogues which provide in-

formation about certain venues in San Francisco on

the restaurants domain (Wen et al., 2015).

Dia-

logues include turns in which each turn is usually a

https://www.repository.cam.ac.uk/handle/1810/251304

Towards Developing Dialogue Systems with Entertaining Conversations

515

pair of utterances between the system and the user.

In reality of human-human conversations, one may

sometimes be silent or in other words ignore the turn.

In this baseline dialogues data set, almost turns are

symmetrical in which each turn usually contains two

utterances of both the system and the user. The base-

line dialogues contain 1066 dialogues with 6198 turns

in which 6 turns on average for each conversation to

obtain about three or four goals including the name,

address, foods, or areas of a restaurant. This means

that the dialogue speed is so fast when almost of the

system’s response can satisfy one of the user’s goals

immediately.

In order to generating interesting and intelligent

dialogues, we design a set of rules using the features

described in Section 2. The proposed ideas are imple-

mented using Java, wehreas the rules and implemen-

tation detail are available at the site.

3.2 Assessment using Game Reﬁnement

Measure

By using the features, the baseline dialogues can be

enhanced to prolong with more turns, and we call the

generated dialogues as enhanced dialogues. Enhanc-

ing more turns leads to a slower speed of dialogues,

where users need to wait more turns to obtain the

goals. Table 2 describes the number of goals and turns

in the baseline and the enhanced dialogues. The aver-

Table 2: Statistics on the number of dialogues, goals and

turns: baseline dialogues and enhanced dialogues com-

pared.

Systems Dialogues Goals Turns

Baseline 1,006 4,249 6,198

Enhanced 1,006 4,249 12,534

age number of goals and the average number of turns

are shown in Table 3. The game reﬁnement value is

calculated based on these two parameters using Equa-

tion (4), where G and T stands for the average num-

bers of users’ goals and turns in dialogues, respec-

tively. Thus we ﬁgure out the game progress model

of dialogues x(t) as shown in Equation (1) to derive

the game reﬁnement measure.

Table 3: Game reﬁnement values: baseline dialogues and

enhanced dialogues compared.

Systems Goals Turns R

Baseline 4.224 6.161 0.334

Enhanced 4.224 12.459 0.165

https://github.com/nguyenlab/DialogueGame

The reﬁnement ratio of the baseline dialogues is

0.334 which is much higher than the balanced ratio of

sophisticated games as shown in Table 1 which is in

the range of 0.076 to 0.078. Meanwhile, our method

obtains a lower ratio than the baseline: 0.165. The

high ratio of the baseline dialogues indicates that the

speed of the dialogues is so fast; in other words, the

outcome of the dialogue can be obtained in few turns

and easy to predict. This is different from interesting

games in which the game’s progress should contain

some uncertainty, and the game’s outcome cannot be

predicted until the endgame. In the proposed method,

users have to wait for more turns to obtain the goal,

which indicates that the progress of the dialogue con-

tains some uncertainty rather than straightforward re-

sponses from the system. This can help the users feel

more interesting in the dialogue’s progress. Nonethe-

less, this should be further improved to obtain the bal-

anced ratio. In addition, the reﬁnement ratio of dia-

logues also needs to be further investigated to ﬁnd a

comfortable ratio for this domain.

3.3 Assessment with Human Subjects

We admitted that the change of feelings and emo-

tion does happen in our minds when playing games

or being in conversations. Nevertheless, it is quite

challenge to measure the exciting or intelligence of

dialogues, which is related to biology and emotion

in our minds. In order to perform this task, human

evaluation experiments were conducted while invit-

ing volunteers to evaluate the dialogues. There were

11 participants (postgraduate students) from various

ﬁelds including natural language processing (NLP),

games, robotics, and general (participants work in

other ﬁelds).

The evaluation contains 20 pairs of dialogues, in

which each pair of dialogues includes a baseline di-

alogue randomly selected from the baseline data set

and output dialogue generated by using the proposed

method (enhanced dialogue). We created a web-based

evaluation page available at

so that it can be easy to

extend and evaluate this task in popularity.

Table 4: Human assessment results. Equal, Better, Worse:

the enhanced dialogue achieves equally, better or worse

score than the baseline dialogue, respectively.

Factors Equal Better Worse

Satisfaction 64.25% 32.40% 3.35%

Interesting 38.55% 57.54% 3.91%

Table 4 presents the human assessment results.

http://150.65.242.105:8080/dialoguesystem/

ICAART 2017 - 9th International Conference on Agents and Artiﬁcial Intelligence

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We considered two factors to compare the baseline

and enhanced dialogues including satisfaction and in-

teresting. The satisfaction indicates the degree that

users satisfy with the responses from the system while

the interesting shows how interesting the dialogue’s

progress. The results show that the enhanced dia-

logues are recognized more interesting than the base-

line dialogues with up to 57.54% of the dialogues.

Meanwhile, the enhanced dialogues still satisfy the

users’ goals, or even the users can obtain more sat-

isfaction with the enhanced dialogues. Speciﬁcally,

64.25% of the enhanced dialogues satisfy the users’

goals equally with the baseline dialogues. Surpris-

ingly, 32.40% of the enhanced dialogues are rec-

ognized more satisfaction with the users’ goals. It

should be noted that the enhanced dialogues do not

always satisfy users or make the dialogue’s progress

more interesting, in which 3.35% of the enhanced di-

alogues do not satisfy the users’ goals as the base-

line dialogues, and 3.91% is less interesting. This can

be explained that users sometimes want to obtain the

goals as fast as possible and only consider the knowl-

edge aspect rather than entertaining; in that case, the

longer dialogues may lead to less satisfaction and less

interesting.

Table 5: Scoring by human subjetcs working in different

ﬁelds.

Area Factors Equal Better Worse

(%) (%) (%)

Robotics Satisfaction 82.50 12.50 5

Interesting 32.50 62.50 5

NLP Satisfaction 61.67 31.67 6.67

Interesting 43.33 53.33 3.33

Games Satisfaction 50 50 0

Interesting 0 95 5

General Satisfaction 59.32 40.68 0

Interesting 50.85 45.76 3.39

The evaluation results are presented in Table 5 to

compare between the study areas. The highest ratios

that the enhanced dialogues are more interesting than

the baseline dialogues are 95% and 62.50%, evaluated

by the participants from games and robotics areas, re-

spectively. Meanwhile, the participants from the gen-

eral ﬁeld evaluated the high ratio that the enhanced

dialogues are better in both satisfaction and interest-

ing factors with 40.68% and 45.76%, respectively.

Table 6 presents the ratios of enhanced dialogues

that obtain the higher scores than the baseline dia-

logues in both factors: satisfaction and interesting.

Participants from the games area evaluated this ratio

up to 50% while the ratios are 33.90% in the general

area and 10% in the robotics area.

Table 6: Ratio of enhanced dialogues that are better than

the baseline dialogues in both factors: satisfaction and in-

teresting.

Area Ratio

All 27.37%

Robotics 10%

NLP 25%

Games 50%

General 33.90%

3.4 Discussion

This research is the ﬁrst effort in applying the game

reﬁnement theory to evaluate dialogues. It is neces-

sary to ﬁnd comfortable values of game reﬁnement

for the dialogue domain as other games shown in Ta-

ble 1. The comfortable values can be seen as the

value’s range of r in Equation (5).

r =

√

(5)

Where G and T stands for the average numbers

of users’ goals and turns in dialogues, respectively.

Finding the comfortable reﬁnement values for dia-

logues is important when we apply the game reﬁne-

ment measure to evaluate attractiveness of dialogues.

In order to explore the balanced or comfortable game

reﬁnement values in the dialogue domain, we need

further investigations using general data of dialogues

in which a large data set of human-human dialogues

should be analyzed for the game reﬁnement’s param-

eters. This should be conducted in future researches.

4 CONCLUDING REMARKS

To our best knowledge, this is the ﬁrst result in the di-

rection to develop dialogue systems with entertaining

conversations while beneﬁting from the game reﬁne-

ment theory. We proposed a method to improve the

current goal-driven dialogue systems which support

users for speciﬁc tasks while satisfying users’ goals

with entertaining conversations. We then developed

a dialogue system in which a set of features are con-

sidered to generate entertaining conversations while

reasonably prolonging the original too short dialogue.

Game reﬁnement measure was employed for the as-

sessment of attractiveness since the conversations in

dialogue systems can be seen as the process when a

player creates shoots or moves to win a game. The en-

hanced dialogues generated by the proposed method

are evaluated by human subjects as well as game re-

ﬁnement measure. The assessment results conﬁrmed

Towards Developing Dialogue Systems with Entertaining Conversations

517

the effectiveness of the proposed method. The present

idea can be a promising way to realize dialogue sys-

tems with entertaining conversations although further

investigations are needed.

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