Information Navigation System with Discovering User Interests
Koichiro Yoshino, Yu Suzuki and Satoshi Nakamura
Graduate School of Information ScienceNara Institute of Science and Technology
8916-5, Takayama-cho, Ikoma, Nara, 6300192, Japan{koichiro,ysuzuki,s-nakamura}@is.naist.jp

Abstract

user’s question and presents additional information proactively according to the detected user’sinterests, even if the user can not find the exact
words to express his or her interests.
The partially observable Markov decision process (POMDP)-based management architecture ofinformation navigation was proposed (Yoshinoand Kawahara, 2014) with several dialogue actsof providing information, however, this study ran
the system only on one limited domain. In thisdemonstration, we used the belief-update function and the policy function trained on the di↵erent domain (news navigation for baseball news)
for the proposed system (information navigationfor tourist) to investigate the robustness of the defined system architecture. We also introduce a newmechanism to select more related topics when thesystem selects a topic to be presented in the next
sub-dialogue by introducing semantic similarity ofdialogue topics (content).
The proposed system has speech interfaces
with open-source speech recognition system Julius(Lee and Kawahara, 2009)1 and text to speech system OpenJtalk2 . This system runs on a standalone
machine without connecting other servers, however, any modules can be replaced by other modules on other severs because modules connect eachother on TCP/IP protocol.

We demonstrate an information navigation
system for sightseeing domains that has a
dialogue interface for discovering user interests for tourist activities. The system
discovers interests of a user with focus detection on user utterances, and proactively
presents related information to the discovered user interest. A partially observable
Markov decision process (POMDP)-based
dialogue manager, which is extended withuser focus states, controls the behavior of
the system to provide information withseveral dialogue acts for providing information. We transferred the belief-update
function and the policy of the manager
from other system trained on a di↵erent
domain to show the generality of defined
dialogue acts for our information navigation system.

1Introduction

A large number of dialogue systems to assist daily
life of users have been deployed in the real world,
however, most are handled on goal-oriented architecture (Young et al., 2010), on which clear goalsof users are assumed. In contrast, an information navigation system (Yoshino and Kawahara,2015) to clarify ambiguous user goals through a
dialogue was proposed. The system provides information about current talking topics with several
dialogue acts of providing information according
to the ky strength of the user’s interest to the current topic. If the user’s intention is ambiguous andthe user does not have any strong interest or focus,the system provides general information about thecurrent discussion topic to help the user decide.
However, if the user has specified interests or focus for some contents, the system answers the2

System Architecture

Information providing is a sub-function of thegeneral purpose function defined in ISO-246172 standard dialogue act set (Bunt et al., 2012),
which includes dialogue acts of providing new orrequested information from the dialogue partner.
We defined seven dialogue modules to fulfill theinformation demand of a user though interactions
12

https://github.com/julius-speech/julius
http://open-jtalk.sourceforge.net/356
Proceedings of the SIGDIAL 2017 Conference, pages 356–359,
Saarbrücken, Germany, 15-17 August 2017. c 2017 Association for Computational Linguistics

Table 1: Relations between defined dialogue modules in our system and dialogue acts defined in ISO24617-2 (Bunt et al., 2012)
Category in ISO-24617-2
DAs in the proposed system
General purpose
Information Providing Inform
Topic presentation (TP)function
Storytelling (ST)
Proactive presentation (PP)
Answer
Question Answering (QA)
Information seeking
Propositional question Confirmation (CO)Dimension specific Social Obligation*Greeting (GR)function
Turn Management
Turn release
Keep Silent (KS)!"#$%&'(")(*&%+,&#(,"-./!"#$%&'(")(-*&'&0,&#(,"-./

8()5"(9&"#:*61'#:*6&4$(1(+"5"*#+&-'4.

!"#$%&'())*+,-!'.

!"#$%&'( )"(&*+4$#56"*7(&4$(1(+"5"*#+-44.

!"#$%!"#$%!"#$%0)*+,%3!"#$%!"#"$%&"'(-'.',('/%()*+,0)*+,%2!!!

/0(1"*#+ 2+13($*+,-/2.

!!!

,"-"(&'*%!"#$%!"#$%&'$%()*+,!"#$%0)*+,%1!"#$%&%'()$)'*+&-!"#$%&%'()$)'*+&,

4(5'"%6)/7.'8!"#$%&%'()$)'*+,-.++*$(/0-12

3++#&,$4+(%+0352

6"(7$.8'*$"(0692Figure 1: Overall architecture of proposed system.
by following this standard. The relations of defined modules and ISO-24617-2 dialogue acts aresummarized in Table 1. We defined three subcategories of dialogue act of “Inform” in the information providing function i.e., topic presentation (TP), storytelling (ST), and proactive presentation (PP), to enable smart information provision.
The dialogue act “Answer” is also implemented as
the question answering (QA) module in the samefunction. Our information navigation system basically provides information, but only one casein which the system uses the information-seeking
function is confirmation (CO) to clarify the previous user intent. Dimension-specific functions support the general-purpose function; thus, minimumsocial obligation and turn-management functions
are implemented as Greeting (GR) and Keep silent(KS).

ager. The call of system action is managed by thepolicy of POMDP, which takes into account the interests of the user (=user focus). The belief update
is defined as,X
b0s0 , f 0 = P(s0 , f 0 |o0s0 , o0f 0 )P(s0 , f 0 |s, f, a)b s, f , (1)swhere s denote the dialogue act of the user, f is
the focus of the user on the current topic, and a isthe selected dialogue module (dash means the nextturn). The policy is trained in Q-learning as⇡(b s, f ) = a(2)

by using defined rewards and a user simulator constructed from dialogue data with users. The details
are described in a previous study (Yoshino andKawahara, 2015). The dialogue data of a di↵erent domain (news navigation for baseball news) isused to train the belief-update function and policy
function for the current domain (sightseeing domain).

Dialogue modules for these functions are defined for the task of news navigation (Yoshino andKawahara, 2015), and the call of these modules at
each turn is managed by the POMDP-based man357

The order of topics (=documents of the information source) to be presented at each subdialogue is pre-defined in the current system.
However, topics to be introduced by the systemshould be selected according to user interests inthe dialogue history, which can be captured in thepast dialogue. Thus, we introduce a mechanism
to introduce new topics that will be more attractive for the user by using past topics that the userand system already discussed. We defined a semantic similarity measure to define the similarities of each pair of topics (=news). The systemdetermines the next topic of the dialogue by using
the similarity from topics that the user is interestedin, to topic candidates to be presented as the next
topic. A binary flag of user interest (=focus) canbe predicted for each presented topic by using theexistence of user focus in user utterances. Only
vectors of flagged documents are used to select thenext presenting document. If TP module is calledby the dialogue manager, the TP module selects
the next dialogue topic according to the similarity.
The system calculates a vector of a document withword2vec (Mikolov et al., 2013) and the longshort term memory neural network (LSTM)-based
encoder-decoder model (Cho et al., 2014) to define
the similarity. Each word of new text is embedded
into a fixed-length distributed representation withword2vec, and inputted into the LSTM-based encoder model. The LSTM-based encoder-decoder
model is trained to restore the original sentences ofnews texts. The final vector of the encoding phasein this architecture is used as the vector of the document. News texts collected from a Kyoto prefecture sightseeing Web site3 was used as the training
data of word2vec, LSTM-based encoder-decoder
model, and content of the dialogue system. We
used cosine similarity between the vectors of documents to calculate the similarities of each topic(news texts). The overall architecture is illustratedin Figure 1.

3est. Thus, the system drastically changes the topic
when the system is requested to change the topic(System 3). In System 4, the user shows an interest with a focus on the word “matcha” and thefocus detection module of the system detects thatthe user is interested in this current topic. The vector of the second topic (document) is used to selectthe third topic in System 5, and the selected topic
is similar to the second topic (about “matcha”).
◆⇣Dialogue example
System 0 (GR): Hello, I would like to introduce you to some interesting places in Kyoto.
Let me know if you are interested.
System 1 (TP): There is a cherry blossom illumination event at the Kamo-gawa river.
User 2 (ST): Could you give me the details?
System 2 (ST): We have an illumination eventat the banks of the Kamo-gawa river from
Oike to Gojo, which is called the “flower corridor”.
User 3 (TP): Thanks, do you have any othertopics?
System 3 (TP): There is a tea ceremony inKyoto.
User 4 (QA): Does it involve matcha (traditional style green tea)?
System 4 (QA): Shoka-do added a newgreen-tea course in a quality tea room.
User 5 (TP): Interesting. Any other news?
System 5 (TP): There is a long line to wait
for a new matcha parfait.
...

✓Acknowledgement
Part of this work is supported by Japan Science and Technology Agency PRESTO (JPMJPR165B). Part of this work have been achievedby ”Research and Development on Fundamentaland Utilization Technologies for Social Big Data”,the Commissioned Research of National Instituteof Information and Communications Technology(NICT) , JAPAN.

Dialogue example (translation fromJapanese)In the following example, the system starts a dialogue from the presentation of the system aboutsome interesting places in Kyoto. In User 2, theuser requires an overall description of the presented topic, however, the user does not haveany specified focus word that indicates his inter3

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