Japanese Dialogue Corpus of Information Navigation and Attentive Listening
Annotated with Extended ISO-24617-2 Dialogue Act Tags
Koichiro Yoshino1,3 , Hiroki Tanaka1 , Kyoshiro Sugiyama1 ,Makoto Kondo2 and Satoshi Nakamura1
1

Graduate School of Information Science, Nara Institute of Science and Technology,
8916-5, Takayama-cho, Ikoma, 6300192, Japan
2
Health Care Center, Nara Institute of Science and Technology, 8916-5, Takayama-cho, Ikoma, 6300192, Japan
3
PRESTO, Japan Science and Technology Agency, 4-1-8 Hon-machi, Kawaguchi, Saitama 332-0012, Japan{koichiro, hiroki-tan, sugiyama.kyoshiro.sc7, s-nakamura}@is.naist.jp, makoto-k@hcc.naist.jp
Abstract
Large-scale dialogue data annotated with dialogue states is necessary to model a natural conversation with machines. However, largescale conventional dialogue corpora are mainly built for specified tasks (e.g., task-oriented systems for restaurant or bus information
navigation) with specially designed dialogue states. Text-chat based dialogue corpora have also been built due to the growth of social
communication through the internet; however, most of them do not reflect dialogue behaviors in face-to-face conversation, including
backchannelings or interruptions. In this paper, we try to build a corpus that covers a wider range of dialogue tasks than existingtask-oriented systems or text-chat systems, by transcribing face-to-face dialogues held in natural conversational situations in tasks of
information navigation and attentive listening. The corpus is recorded in Japanese and annotated with an extended ISO-24617-2 dialogue
act tag-set, which is defined to see behaviors in natural conversation. The developed data can be used to build a dialogue model based on
the ISO-24617-2 dialogue act tags.
Keywords: Dialogue corpus, dialogue act, information navigation, attentive listening

1. Introduction
Spoken dialogue systems have been modeled with abstracted classes, such as dialogue states, which are handcrafted for the assumed task of the system (Dahl et al.,
1994; Williams et al., 2013; Kim et al., 2016). However,handcrafting such dialogue states is costly, making it harder
to build extensible dialogue systems for a variety of domains. Two solutions are widely used to avoid this problem: building a multi-domain system (Gašić et al., 2015;
Papangelis and Stylianou, 2017) and building a system withmore generalized classes (Yoshino et al., 2017; Keizer andRieser, 2017). However, the approach of building a multidomain system is an extension of using existing slot-value
type dialogue states, and it is hard to apply this architecture to dialogue tasks that do not assume language understanding methods based on slot-filling, such as information
navigation (Yoshino and Kawahara, 2015) or attentive listening (Yamaguchi et al., 2016; Lala et al., 2017; Tanaka
et al., 2016). In comparison, general dialogue acts are
more effectively used in these tasks because the approachof using them separates the functions and content of dialogue and only focuses on modeling dialogue functions. In
other words, the approach of using generalized dialogue act
classes does not have the problem of exponentially increasing the number of dialogue states, which is caused by combinations of dialogue content.
ISO-24617-2 (Bunt et al., 2010; Bunt et al., 2012) is defined as a standard of dialogue act classes that is based oncommunicative functions of utterances in dialogue. It is
important to realize natural dialogue in order to use more
appropriate dialogue functions (acts) that match dialogue
history and the context (Mizukami et al., 2016). We previously reported that dialogue systems can be effectively
managed by using a part of the ISO-24617-2 dialogue act

classes (Yoshino et al., 2017). It was also indicated in thiswork that the dialogue policy learned in this architecture
can be applied to different domains because it only models the behaviors (functions) of the system in a information
navigation task.
Switchboard corpus (Godfrey et al., 1992) is also a corpusthat is annotated with the discourse tag-set Discourse Annotation and Markup System of Labeling (DAMSL) (Coreand Allen, 1997); however, ISO-24617-2 is designed tomodel dialogue behaviors in natural conversation more directly than the DAMSL tag-set. The problem with using
ISO-24617-2 dialogue act classes is that there is no large
scale publicly available dialogue data annotated with thisannotation standard.
The task of collected dialogue data is also important. Existing dialogue are categories into two types from the viewpoint of their task: task-oriented dialogue and non-taskoriented dialogue. Task-oriented dialogue systems assume
actual goals of dialogue compared with non-task-oriented
dialogue systems do not define any actual goal. The problem of task-oriented dialogue is that the number of dialogue
behavior observed in the conversation is limited because
stereotyped expressions mainly achieve the task of the dialogue. On the other hand, free conversation that does not
set any dialogue goal contains a large number of behaviors, which is hard to implement everything in dialoguesystems. Information navigation and attentive listening aretasks in a good position between them; goals of these tasks
are more ambiguous than task-oriented dialogue, but tasks
or domains can be limited in the task definition. Thus, we
focus on collecting dialogue corpora of information navigation and attentive listening in this paper.
We recorded 60 face-to-face dialogues, 20–30 minutes for
each, in the tasks of information navigation and attentive

2922

listening. Each utterance was transcribed and annotatedwith extended ISO-24617-2 dialogue act classes by two disciplined annotators.

video
microphonenavigator-listener
Recording equipments

2.

Recording Procedure

2.1. Scenario

user-speaker

Information navigation is a task that involves one participant (navigator) introducing information written in documents (news, description of sightseeing places, etc...) to another participant (user). For our recording scenario, we prepared positive and negative content for the navigator. The
navigator introduced the prepared information (news) bygiving a summary and details, answering the user’s questions, and proactively presenting corresponding information. The user could ask any questions or request information corresponding to the introduced topic. Once thephase of information navigation was finished, the dialogue
task was changed to attentive listening. The user of information navigation stated one’s opinion on the topics introduced during the information navigation (speaker), and thenavigator of information navigation attentively listened totheir opinion by using listening techniques (listener). Participants of one dialogue repeated this procedure for each
prepared information source.
We recruited 24 elderly people for the user-speaker roles,
because we focus on dialogue modeling to talk with elderly
people, which is caused by the culture-specific demand ofJapan. For the navigator-listener roles, there were 5 professional counselors, 5 professional care-takers, and 5 students
of graduate school (15 in total). Every attendee was a native
Japanese speaker. Each navigator-listener talked with 4 different user-speakers; in other words, 60 dialogues that had
unique combinations of participants were recorded. The
gender distribution is shown in Table 1.
Table 1: Number of recorded dialogues from the viewpointof gender
speakermale female
listener male23
2
female29
6
The following instructions were given to the navigatorlistener participants.
• The navigator-listener reads two different documents(news) as dialogue topics before the dialogue. The information sources include positive and negative topics.
The navigator-listener can check the documents anytime during the dialogue, but it is forbidden to show
the documents to the user-speaker directly.
• The dialogue should start from a positive topic. The
navigator-listener will decide to change the topic after 10 minutes is spent on one topic depending on thecontext of the conversation.
• The navigator-listener describes the main points of thecurrent topic first of all and asks for the user-speaker’s
opinion. Self-introductions are allowed as an icebreaking.

microphone

video

Figure 1: Recording room
• Digressing is allowed if it is natural in the context ofthe conversation because we want to balance the naturalness of dialogue and control of dialogue situation.
The following introductions were given to the user-speaker
participants.
• If you have any questions on the description given by
the dialogue partner, you can ask them anytime.
• After the description of the navigator-listener, you
should give your opinion on the current topic.
• Digressing is allowed if it is natural in the context ofthe conversation.
After the recording, each participant was guided to different rooms for questionnaires. The following items were
evaluated with 7-degree scores: smoothness, trustiness, familiarity, empathy, interest of the partner, distance of thepartner. One yes/no question, “did you have any thing thatyou could not talk about with the partner,” was also asked.
We informed participants that the results would not be revealed to the dialogue partners.

2.2. Recording Environment
We recorded dialogues in conference rooms by using headset microphones1 and videos2 to record the upper half of the
speakers bodies. The speech of both speakers was recorded
on a single channel of stereo audio per speaker, though a
USB-audio device3 . Participants sat opposite each otheracross a table. The position of each piece of equipment isshown in Table 1.
We set up the recording as face-to-face because of the timing of backchanneling or interruption is a very critical factor in natural conversation. If we record the dialogue in a
non-opposite situation, behaviors of users in backchanneling or interruption will be different from the face-to-facesituation because dialogue participants generates their behaviors by using not only audio information but also visual
information that can be observed from the dialogue partner.
Frame number of each utterance in the dialogue is also annotated in the transcription phase of our data construction,
which will be used to analyze generation timing.

2923

1

Crown CM311 Headworn Condenser Microphone
SONY HDR-CX670 and HDR-PJ675
3
Roland Quad Capture2

Table 2: Annotation standard used for transcription
Tag names
Tags
Sound prolongation at end of words <H>
Reactive token(R *): reaction without content words (e.g. back-channel)
Filler
(F)
Laugh
(L *)
Short pause(P): 500 msec or longer pause
Inaudible speech(?)
Other standards
Expressions in corpus
Correction by speaker{correction|pronunciation|corrected words}
Numbers
Chinese numerals
English words
Katakana expressions
Lazy speech
Transcribe as pronounced
Table 3: ISO 24617-2 dialogue act definition
Top category

Sub-category

Tags

GeneralpurposeInformation seeking
Information providing
Commissive

question, propositional q., check q., set q., choice q.,
inform, agreement, disagreement, correction, answer, confirm, disconfirm
offer, promise, address request, accept request, decline request,
address suggest, accept suggest, decline suggest
suggest, request, instruct, address offer, accept offer, decline offer
auto positive, allo positive, auto negative, allo negative, feedback elicitation
turn accept, turn assign, turn grab, turn keep, turn release, turn takestalling, pausing
completion, correct misspeaking, signal speaking error, self correction
interaction structuringinitial greeting, return greeting, initial self introduction,
return self introduction, apology, accept apology, thanking,accept thanking, initial goodbye, return goodbye

Dimension
specific

Directive
Auto/allo-feedback
Turn management
Time management
Own/partner comm. man.
Discourse structure man.
Social obligations man.

3.

Annotations

3.1. Transcription
To use the data for dialogue modeling, we transcribed every utterance included in the conversation of each session.
The transcription standard used is given in detail in Table 2.
Annotators automatically inserted a short pause if they observed a 500 msec or longer pause and created segments ofutterances according to the content of the utterances. Time
stamps were annotated at the beginning and ending pointsof each utterance. As the result of transcription, there were
27,986 utterances, 322,684 words, and 490,705 charactersin 60 dialogue sessions. Each utterance was segmented
with the Japanese morphological analyzer KyTea4 (Neubig
et al., 2011).

seeking functions, information-providing functions, commissive functions, and directive functions. Dimension
specific functions, additional functions that have specificroles for making a conversation advance smoothly, consist of six sub-functions: auto/allo-feedback functions,turn-management functions, time-management functions,own/partner communication management functions, discourse structure management functions, and social obligations management functions. Several tags that belong todimension specific functions can be used for one utterance
if the utterance has several roles for making a dialogue advance. Fifty-five detailed classes are defined under the subfunctions. Our dialogue act annotation was processed in accordance with the following procedure to follow the structure of the definition of ISO 24617-2 dialogue acts.

3.2. Dialogue Act Annotation
We annotated each utterance of recorded dialogue with the
ISO 24617-2 dialogue act annotation standard (Bunt et al.,2012). The standard has hierarchical classes that are related to the decision process of dialogue acts. A summary of the original structures of defined functions andtags is shown in Table 3. There are two functions in
the top hierarchy: general-purpose functions and dimension specific functions. General purpose functions, functions that classify utterances from the viewpoint of dialogue content, consist of four sub-functions: information4

http://www.phontron.com/kytea/

2924• Decide a sub-function of an utterance from the subfunctions of general purpose functions. If there is nomatched function, use the label “dimension specific,”
which means that the utterance has only the role ofmaking the dialogue advance.
• Decide a specified category (tag) of an utterance fromthe tags defined for the sub-function. If “dimensionspecific” is selected, annotators select a tag from anyof the functions of the dimension specific functions.
• Watch all utterances again to add additional tags fromdimension specific functions, because one utterance
can take several roles of dimension specific functions.

ing data was annotated by single annotator.

Table 4: ISO 24617-2 dialogue act definition
Original tag (sub-function)
Extended classes
Inform (information-seeking) Topic presentation
Storytelling
Proactive presentation
Auto positive & negative
Auto neutral(auto/allo-feedback)

Table 5: Numbers of basic tags in transcription
Tag name
Numbers
Filler12,549Reactive token
22,080Laugh
2,256

To adapt the annotation standard for our purpose, modeling of information navigation and attentive listening, we
added the several dialogue tags mentioned in Table 4. The“inform” tag was annotated on many utterances in the information navigation task; thus, we divided the tag into
three classes according to the kinds of information being
provided. “Topic presentation” is the action of providing a
new topic in conversation. With these utterances, new topics are introduced or largely changed from the previous topics. “Storytelling” is an action done mainly after a “topic
presentation.” In storytelling, the details and main points ofa provided topic are presented. “Proactive presentation” isthe presenting of additional corresponding information to
the current topic.
We also extended the auto/allo-feedback functions by defining a neutral state for feedback for the dialogue partner. In
Japanese conversations, there are many unclear feedback
responses given to partners such as ones containing modesty and compliments. The “auto neutral” tag was used forsuch responses for which it was hard for annotators to decide whether the responses were positive or negative.

3.3. Annotation and Feedback Loop
We had loop processing of annotation and feedback to realize accurate annotation of dialogue acts. In the first step,
two annotators annotated tags in the same part of data without any comparing and fitting of annotations. We used
Fleiss’ kappa (Fleiss et al., 2013) to calculate the agreement of two annotators rather than Cohen’s kappa, because
we allowed annotators to put several dialogue acts for one
utterance. In the first loop, we calculated the annotationagreement of the first step of the annotation: the decisionof the sub-function in “General purpose” or others (=anyfunctions of “Dimension specific”). The annotation agreement between two annotators was 0.768 for the decision.
The score is sufficiently high, but we took a meeting for
fitting their annotation after the first annotation step. We
added some case-studies of annotation in the annotation
standard and tried to annotate other portion of the dialogue
data. We took the loop of annotation and feedback twice.
After these loops, the final annotation agreement of topcategory was 0.786. We also calculated the agreement scoreof tags, it was 0.485, even if we have a variety of tags.
This high agreement is probably caused by the bias of the
distribution of tags, which is brought by the property oftasks of the collected dialogue. Tasks of information navigation and attentive listening define roles of speaker andlistener to participants explicitly. This property increases
the number of backchannels of listeners, which is easy toannotate. After the annotation and feedback loops, remain-

Table 6: Numbers of sub-functions in annotated corpus
Tag name
Numbers
Information seeking
3,066
Information providing
8,527
Commissive
69
Directive
74
Auto/allo-feedback
17,186
Turn management
718
Time management974
Own/partner comm. man.
871
Discourse structure man.
163
Social Obligation man.
465

4. Statistics of Corpus
The numbers of tags for the transcribed utterances areshown in Table 5. The majority of “reactive token” wasbackchannels in information navigation and attentive listening. The numbers of each dialogue function in the annotated corpus are shown in Table 6. The sub-function that
had the largest number was “Auto/allo-feedback,” which
included positive and negative feedback tags for user utterances. This function is one of the most important functions
for realizing information navigation or attentive listening
systems. “Information seeking” and “Information providing” also often happened because summaries and opinions
on the topic being talked about were often exchanged between dialogue participants.

5.

Dialogue Example

We show an dialogue example in Table 7. This example
shows that the dialogue is started from the information providing by the navigator about the news of Nobel prize. The
user made some confirmation questions to comprehend thedetail of the news in following turns. After the phase of information navigation, the dialogue was moved to the attentive listening phase. In the attentive listening phase, the user
(speaker) tried to talk their opinion about the news, and thenavigator (listener) listened to the talk of the user carefully.
Some techniques of attentive listening, backchanneling or
repeating, are used in the attentive listening phase.

6.

Conclusion

We recorded natural face-to-face Japanese conversations intasks of information navigation and attentive listening andtranscribed utterances to build a dialogue corpus. Each utterance was annotated with extended ISO24617-2 dialogue
act tags to use the data for dialogue modeling. We developed a dialogue act annotation standard by using feedback2925

to the annotated results and finally achieved the high agreement of annotation results.
It is expected that the collected dialogue data contains techniques of information navigation or attentive listening ofprofessional counselors or professional care-takers, which
will contribute the dialogue modeling of each task. In the
future, we plan to use the data to improve the dialogue
model for both tasks.

7. Acknowledgement
This work is supported by JST PRESTO (JPMJPR165B).

8.

References

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Table 7: Dialogue example annotated with dialogue act tags
Start
End Transcription (Translation)
2975737293017460 本日 私 から (F えっと) 二 本 ニュース を 紹介 さ
し て 頂 き ま し て 、 それ に つ い て (F えっと)
お 話 し さ し て 頂 く って 言 う 、(Today, I introduce two news, then we will discuss
about the news,)** まず 一 つ 目 は (F えっと) ノーベル 賞 の 話 な ん で す
けど (F とー) 東京 工業 大学 の 名誉 教授 の 大隅 さん と言 う 方 が (F えっと)(P) ノーベル 賞 受賞 さ れ ま し た 。
で 、 この お 父 さん も (F えっと) 鉱物 学 者 (F あの)
理系 の 学者 一家 (P) の 末っ子 で (F とー) とうとう末っ子 に し て 悲願 を 達成 する と 言 う (P) 事 で(The first news is about Nobel prize, an honorary professor
of TITECH, professor Ohkuma won the Nobel prize. His
father was also a researcher, and his family was researchfamily. He was youngest child but achieved long-cherishedwish of the family.)** (F えっと) この ニュース って ご 存 じ で し た
で しょ う か ？
Do you already know the news?
30469792
33626875 (R ふん)(Backchanneling)
37591667
44030625 (R うんうん)(Backchanneling)ID
LI-000001

Tag
DS-IS
DS-OP

LI-000001-2

IP-TP

L1-000001-3

IS-CEQ

SP-000001

AA-AP

SP-000002

AA-AP

...
SP-000009

IS-CEQ

304958125

336606042

LI-000002

IP-CO

334910658

341053515

SP-000010

344795000

369141250

LI-000003

IP-AN
AA-ANE
IP-PP

369885488

381051927

SP-000011

IS-Q

422951042

435910000

LI-000004

IP-AN

435078005

483383447

LI-000004-1**

SP-000012

IP-CO
AA-ALP
IS-CEQ

464000000

504021875...
SP-000138

IP-PP

10445199375

10629265625

LI-000222

AA-AP

10472939456

10477368481

LI-000223

AA-AP

10533057370

10541755556

LI-000224

IS-CEQ

10617459184

10657617914(P) オオスミ さん い う 方 で す ね 。
(Professor Ohkuma.)(R はい)(Yes.)
聞 い た よう な 聞 か ん よう な 感じ で す ね 。
(I’m not sure I have heard or not.)こう い っ た 方 で す 。
(This is his picture.)
これ 何 年 位 前 で し た っけ?。
(How many years ago he won the prize?)
多分 (F えっと) これ が 今年 の 話 じゃ な い か と僕は思う。
(Probably, he won the prize in this year, I guess.)(R はい) 今年 。
(Yes, in this year.)
今年 、(F あ) 今年 で す か 、(Oh, in this year.)
で 、 我々 の 村 から 高校 行 く と い っ て も ね 、なかなか そう 、 当時 ね 、 私 ら も 貧乏 だっ た けど
なん と か (P)(F ま) 高校 だけ は 行 か な い かん と い う
気持ち で 、(P)(F ま) 出 て き て 行 っ た けど も 、(F あのー) なかなか 、 三 分 の 一 ぐらい し か ね 、(P) 高校 に 行 か な かっ た で す 。
(In my generation, I was also poor, but Igo on to high school
because believed that I should go on to, but only one-thirdof my friends go on to high schools.)(R はい)(Backchanneling)(F あー)(R はい)(Backchanneling)(F あー) 周り の 同じ 同 世代 が 。(R ほー)(Oh, your same generation people,)

2927