ｌＥＩＣＥＴＲＡＮＳＩＮＦ.＆ＳＹＳＴ.，ＶＯＬＥ７６－Ｄ，Ｎｏ．ｌＪＡＮＵＡＲＹｌ９９３
8４

ＰＡＰＥＲＳｐｅ鯛川ssueonSpeecfDa"。、応cou'雪ｅＰｍｃｅｓｓｍｇｍＤｊａﾉbgueSystcms

ASpokenDiaIogSystemwithVeriでicationand
CIamicationQueries

ＭｉｋｉｏＹＡＭＡＭＯＴＯＴ，Ｍｃｍ６助Satoshi KOBAYASHIT,YujiMORIYAT,１V､"ｍｅ肋ｅｌｌＳ
ａＭＳｅｉｉｃｈｉＮＡＫＡＧＡＷＡＴ，Ｍｂｍ６ｅｒ

ＳＵＭＭＡＲＹＷｅｓｔｕｄｉｅｄｔｈｅｍａｎｎｅｒｏｆｃｌａrificationand

verificationinrealdialogsanddevelopedaspokendialogsystemthatcancopewiththedisambiguationofmeaningsofuserinput

utterances・Weanalyzedcontent，querytypesandresponsesof
humanclarificationqueries、Inhuman-humancommunications，
tenpercentofallsentencesareconcernedwithmeaning
clarification、Therefbre,inhuman-machinecommunications,ｗｅｂｅｌｉｅＶｅｉｔｉｓｉｍｐｏｒｔａｎｔｔｈａｔｔｈｅｍachineverifiesambiguities
occurringindialogprocessing・Weproposeanarchitecturefbradialogsystemwiththiscapability・Also,ｗｅhaveinvestigatedthesourceofambiguitiesindialogprocessingandmethodsof
dialogClarificationlbreachpartofthedialogsystem
Aayw｢んｎａｍｒａノル"gzJageProceFsz"aSPeecﾉｉ花cOg"i[わ"，伽ﾉＤｇＳＷｅｍ,γe航cα肋",ｃﾙﾉ･（/7Ｍｍ〃
１．Introduction

Recentresearchindialogsystemshasshowna

significantimprovementoverearlierquestion-

answeringSystems・Concernisfocussedonamecha-

nismfbrmorenaturalconversatlon．
.（'肌Theultimate

systemshouldhavepowerfillinferenceabilityand

becomeimportantiromtheviewpointofthepracticaluseofnaturallanguagedialogsystems.(2),(3)2．AnalysisofVerificationandClarificationwithin
HumanDialog

Ｉｎｔｈｉｓｓｅｃｔｉｏｎ,weanalyzethecontent,types，and
responsesoftheverificationandclariifcationqueryintranscriptsofhuman-humanspokendialog．

Z1TranscriptsofHuman-HumanSPokenDialog
Weanalyzednineteentranscripts（1769Japanese
sentences）ｉｎthecontinuousspeechcorpusfbrresearchoftheAcousticalSocietyofJapandTheywerecollectedfi｢omtelephoneconversationsbetweenJapanese・
Theyareinfbrmationseekingdialoｇｓｓｕｃｈａｓａｔａ
ｔｒａｖｅｌｂｕｒｅａｕｏｒｏｆｏｔｈersuchconsultations．

knowledge，allowingittodisambiguateintheprocessofanalyzingthestructureandmeaningofsentences，２.ZAnalysisoftheContentsofVerificationand
ClarificationQueries

intention・However，naturallanguageconversation
withmachinesstillcannotguaranteereliablewide-

Weanalyzeverificationandclarificationqueries
fortheambiguoussentences・Thefbllowingsentences

aim，ｍａｎｙｐｒｏｂｌｅｍｓｒｅｍａｉｎｔｏｂｅｓｏｌｖｅｄ

sation，weoftenaskquestionsfbrverihcationor

queries（thesecondsentenceoftheexamples)．We
definethatverificationqueriesaskwhatanotherperson
explicitlystatedandclarificationqueriesaskwhat
anotherpersonimplicitlyｓａｙｓｏｒｄｏｅｓｎｏｔｓａｙ・IntheexamplesQandArepresentaquestioner
andananswerer，respectively・Ｎｏｔｅｔｈａｔｉｎｅｘａｍｐｌｅ(5),sincetheanswerersuddenlyspeaksaboutafacsim‐
ilenotrelatedtothepreviouscontext,thequestioner

systemshouldactivelyinteractwiththehumanuserfbr

ileandthecontexLThequestionerrepresentshislack
ofunderstandingwith“Pardon？"・
CIarificationqueries

resolveellipses，andinfbrpronounreferencesanduser

ranglngconversatiｏｎ、Ifnaturalconversationisthe

Ａｓｆｂｒｔｈｅｉｎｆｉｅｒｅｎｃｅofconcealeduserintentions

lnanutterance,acorrectinfierencebenefitstheuser・In

contrast，awronginferenceofintentiononlyconfUses

theusemIntheworstcase,misunderstandingoccurs．
Ａsfbrambiguityatthecontextlevel，disambiguationmaybedifTicultevenfbrhumanslndailyconver-

arecategorizedintoverihcationandclarification

clariiicationconcerningthemeaningofanutterance・
Sincetheabilityofamachinetodisambiguateis
infbriortothatofmanatthecurrentstate，adialog

cannotunderstandtherelationshipbetweenthefncsim-

helpincasesofambiguitiesMoreover，webelievea(1)infbrmationseekingtoanswerquestions

systemthatcancalTyonareliableconversationwill
ManuscriptreceivedJune22，１９９２．

ManuscriptrevisedAugust26，1992．

↑ＴｈｅａｕｔｈｏｒｓａｒｅｗｉｔｈｔｈｅＦａｃｕｌｔｙｏｆＥngineering，

ToyohashiUniversityofTeChnology，TOyohashi-shi，４４１
Japan．

Q〉ikurakakarimasuka？

（Howmuchdoesittake?）
Ａ＞ninzu-wanan-nindesuka？

（Forhowmanypersons?）

ＹＡＭＡＭＯＴＯｅｔａｌ：．ＡＳＰＯＫＥＮＤＩＡＬＯＧＳＹＳＴＥＭＷＩＴＨＶＥＲＩＦＩＣＡＴＩＯＮ
8５(2)refinementofmeaning（Hotel”North，，）

Q〉kisha-wotsukaitaindesukedo．

。＞NOsu，，？

（rdliketotakeatrain.）（，，North?,?）Ａ＞sorQiashinkansendesune．

Ａ＞ee-soudesu．

（Ｙｏｕmeantheshinkansensuperexpress，
right7）(3)decidingthelevelofanswer
Q＞michijun-woOshietehoshiindesukedomo．

（Couldyoutellmetheway?）（Yes.）

Wecategorizequeriesintothreetypes：“yes/no，，，"what，,ａｎｄ“which”types・For"yes/no”queries,ｏｎｅｃａｎｓｉｍｐｌｙｒｅｐｌｙ‘`yes,，ｏｒ“ｎｏ"・Ｆｏｒ‘`what，，queries，
oneshouldanswerwithavalueofsomeobject・Ｆｏｒ"which,，queries，ｏｎｅｍａｋesadecisionamongthe
alternativeｓＴｈｅｒｅｓｕｌｔｏｆｔｈｉｓａｎａｌｙｓｉｓｉｓｓｈｏｗnin

Ａ〉eQToukatta-onsen-waoideni

TableLnattakotogozaimasuka？

（HaveyoueverbeentoToukatta-onse、?）(4)Meaningofword
A＞Ryuou-toiutokorodesu．

Sinceinfbrmationseekiｎｇｑｕｅｒｉｅｓｔｏａｎｓｗｅｒｑｕｅｓ－ｔｉｏｎ（１）arenotmuchconcernedwithclarification,wecanregardthemasordinaryquestions・Excluding(1)，thereare94verificationandclarificationqueries
However，sinceansweresindialogaccompanyquestions，ａｔｌｅａｓｔｔｗｉｃｅｔｈａｔ，orl88sentencesconcern

verificationandclarificationThatis,tenpercentofall（AplacecalledRyuou.）

sentenCesarerelatedtoverihcationandclarification
Thisresultisobtainedfieomhuman-humanconversa-。〉“Rynou''一wachimeidesuka？

（1s“Rynou,,thenameofaplace?）(5)representationofnotunderstandiｎｇ

tions、Themachinedialogsystemmayaskmore
questions・Thisisparticularlyimportanttoaspoken
dialogsystembecauseoftheambiguitiesinthespeech
recognltlonprocessＡ〉flakkusu-deokurimashouka？

Therearemanyverihcationsandclarificationsof
sentencｅｍｅａｎｉｎｇｓａｎｄｏｆｐａｒｔｉａｌｏｒｗｈｏｌｅｓｅntences・（MayIsenditbyfacsimile?）

Ｔｈｅｓｅｓｈｏｗｔｈａｔｉｔｉｓｉｍｐｏｒｔａｎｔｔｏｕｎderstandexactly

Ｑ＞ｈａ？

themeaningorintentionofauserutteranceandthat
evenahumancannotalwaysunderstandtheseimmedi-（Pardon?）ately，Clearlythesetypesofqueriesareimportantfbr
acomputerdialogsystemusingcurrenttechnology．

verificationqueries(6)meaningofsentence
A＞Kyou-fU-nokaiseki-ryouri-gayumeina
tokoro-gaMotohakone-toiutokoro-ni…

（ThereisarestaurantfamousfbrKyotostylecuisinelunchesinMotohakone.）
Ｑ＞Hakone-deKyou-ltidesuka？

（KyotostyleinHakone?）

Z3AnalysisofAnswerstoQueries

Whenoneasksgeneralverificationquestionssuch
as“Pardon？,,,inmanycasestheotherpersonrespoｎｄｓ
ｗｉｔｈａｍｏｒｅｃｏｍｐｌｅｘａｎｄｌｏｎｇｅｒａｎswerthanthepreviousambiguousstatementlngeneral,itisdifIicult
fbramachinetounderstandacomplexandlong
utteranceTofacilitatemachineunderstandi､９，ｔｈｅ(7)wholesentenceorsentencefragment
Q＞dekirebawah-gaiindesuga．

（Ifpossible,IprefierJapanesestyle.）
Ａ＞wafridesuka？

TablelCategorizationofverificationandclarificationqueries．

(1)
(2)
(3)
（４）
(4)
(5)
(6)
(7)
(8)

（Japanesestyle?）。＞hai．
（Yes.）(8)soundofsentencefragment

ｔｏｔａｌ

A＞hoteru，,N6su，，．

■

yes/nｏ

wｈａｔ

wｈｉｃｈ

ｔｏｔａｌ
tｏｔａｌ

1０

1８

６

3４

2４

０

０

2４

６

０

０

６

２
０

０
１

０
０

２
１

1６

０

０

1６

3７

０

０

3７

６
1０１

２

０

2１

６

８
1２８

1ＥＩＣＥＴＲＡＮＳ、ＩＮＦ．＆ＳＹＳＴ.，ＶＯＬＥ７６－Ｄ，Ｎｏ．ｌＪＡＮＵＡＲＹ１９９３
8６

machineshouldgenerateaquerytowhichanotherpersonrespondswithashortandsimpleanswer・In
thissection，ｗｅａｎａｌｙｚｅｔｈｅｔｙｐｅｏｆｑｕｅｒｙｗｉｔｈｗｈｉchthemachinecanobtainashortandｓｉｍｐｌｅａｎｓｗｅｒ・
TherearecharacteristicpatternSfbreachtypeof
query・Ｗｅｓｈｏｗｔｈｅｒｅｓｕｌｔｏｆａｎａｌｙｚｉｎｇｒｅｓｐonsesinto

querytypcsinTable2．“Yes”ｏｒ"no”ｏｆthe"yes/no,，typemeansthattheiirstwordoftheａｎｓｗｅｒｉｓ``yes，，ｏｒ"ｎｏ"．“Complex,,ｍｅａｎｓｔｈａｔｔｈｅａｎｓＷｅｒｉｓａｃｏｍｐｌｅｘ
ｓｅntencewhere‘`yes，，ｏｒ“ｎｏ,,canbeunderstoodby
inference．“Repeat,，ｍｅａｎｓｔｈａｔｔｈｅａｎｓｗｅｒｉｓａｒｅｐｅａｔ
ｏｆｐartofthequestion．“Nothing,,meansnoanswer．
"Ｒｅｓｐｏｎｄ，，ｏｆ``which，，typequerymeansthatanotherpersoncananswerand“norespond,，meansthats/hecannotanswer．“Ａｄｄｅｄｉｎｆｂ.,，ａｎｄ“ｎｏａｄｄｅｄｉｎｆｂ.”

ｍｅａｎｔｈａｔｔｈｅａｎｓｗｅｒｉｎｃｌｕｄｅｓａｎｄｄｏesnotinclude

informationinadditiontotheliteralanswer，respec‐tively・Ａｎｓｗｅｒｓｏｆ‘`yes,，，‘`ｎｏ,，ａｎｄ‘`repeat，,canbe
understoodbythemachine，andinparticular,ｔｈｅ“noaddedinfb.，,typeisveryeasilyunderstood,becausethe
typeorpatternofanswerisrestrictedSuchanswers

makeup70percentofallanswersofthe"yes/no,，ａｎｄ"which”queries、Therefore,questioningby“yes/no，,

ｏｒ“which,，queriesshouldpromotemachineunderstanding
Theresultsinthepreｖｉｏｕｓａｎｄｉｎｔｈｉｓｓｅｃｔｉｏｎｄｏ
ｎｏｔａｐｐｌｙｔｏａｌｌｋｉｎｄｓｏｆｄｉａｌｏｇＨｏｗｅｖｅｒ,theresults
are，ｗｅthink，generalfbrtheinfbrmationseeking

dialogthatisanimportantportioｎｏｆｔｈｅｋｉｎｄｓｏｆ
ｄｉａｌｏｇｆｒｏｍｔｈｅｖｉｅｗpｏｉｎｔｏｆｔｈｅman-machineinterface・Inthefollowingsections，wedescribeaspoken

dialogsystemthatcangenerateaverificationor
clarificationquerybasedonanextensionoftheanalysisofhumaｎ－ｈｕｍａｎｃｏｍｍｕｎｉｃａｔｉｏｎｓｔｏｍａchine‐humancommunications．

3．SpokenDialogSysteln
3・ｌ０ｕｔｌｉｎｅ

Inthissection，wedescribeourspokendialog
systemwhichsimulatesafictitiousMtFUjitravelbureau・Thereasonfbrselectingthisdomaiｎｉｓｔｈａｔ
ｔｈｅｒｅａｒｅｍａｎｙｓｔｕｄｉｅｓｆｏｒｎaturallanguageinformationretrievingsystems、Ａｌｓｏ，asystemforsightseeing
infbrmationhasvalueinrealworldapplications．
Table2Categorizationofresponses．
ｎｏａｄｄｅｄｉｎｆｏ．
ｎｏａｄｄｅｄｌｎｆｏａｄｄｅｄｌｎｆｏ
ａｄｄｅｄｉｎｆｏ．

【

ｙｅｓｃｏｍｌｅｘcｏｍｐｌｅｘ
０

6６
６６１２
1２

ｎｏ
０
ｎｏ５
５

０ＯｌＯ
1０

ｒｅｅａｔ
ｒｅｐｅａｔ６ｒｅｅａｔ６０
０

（２）Ｏ０
（２）
(2)
ｒｅｓｐｏｎｄ３
1
ｒｅｓｐｏｎｄ３１

nｏｔｈｉ
ｎｏｔｈｌｎｇ、９Ｗｈ０nｏｒｅｓｐｏｎｄｎｏｒｅｓｐｏｎｄＯ２
２

FigurelshowsthecompositionofthesystenLThespeechrecognitionpartoutputsaphoneme（orword）

ｓｔｒｉｎｇｏｆａｎｕｔｔｅｒａｎｃｅａｓｉｎｐｕｔｔｏｔｈeunderstanding
part・Theunderstandingpartistheextensionofour
question-answeringsystemdevelopedearlier、This
partanalyzesthemorphologicａｌａｎｄｂｕｎｓｅｔｓｕ(phrase）structureandthenthedependencystructure
betweenphrases・

Thedependencystructureistransfbrmedintoa

semanticnetworkThesemanticnetworkissenttothe

contextprocessingpartthatdeterminespronounrefbrencesandresolvesellipsesbasedonthecontext・The
contextisthesetofprevioussemantiCnetworksof
inputsentencesthatarestOredinthecontextstack,ａｎdatemporalsemanticnetworkfbrpredictingthenext
userutterance・ＷｉｔｈｔｈｅｕｓｅｏｆｔｈｅｓｅｍａｎｔｉｃｎｅｔｗｏｒｋcomPleted
accordingtocontextanddialogrules，theutterance
generatingpartgeneratesthenextutteranceofthe
system・Inthespeechgenerationpart，thegenerated
utteranceistransfbrｍｅｄintorealspeech

Whenanambiguityoruncertaintyarisesinthe
aboveprocessing，ｔｈｅｓｙｓｔｅｍｐｕｓｈｅｓｔｈｅｓｔａｔｕｓａｔｔｈａｔ
pointintothesystemstackandactivatesverihcationor
clarificationdialogstodisambiguateit・Ａｔｔｈｅｅｎｄｏｆ
ｔｈｅｄｉａｌｏｇ,ｔｈｅｓｙｓｔｅｍｐｏｐｓｔｈｅｓｔｏｒｅｄｓｔａｔｕｓｆｒomthe
systemstackandresumesprocessingfiomthepointof
ambiguityoccurrenceThusthesystemrecursively
callstheentiredialogsystemtoverifyorclarifyambiguousdialogs．
3.2SpeechRecognition

Weusetheconnectedwordrecognitiｏｎsystemdevelopedinourlaboratory.(4)ＴｈｅｒｅcognitionsystemisbasedonawordspottingtechniqueandafiBame

synchronizedbeamsearchwiththeconstraintsof
context-freegrammar・

A1thoughthespeechrecognitionpaｒｔｒｅｑｕｉｒｅｓａ
ｇｒａｍｍａｒｂｙｗｈｉｃｈｉｔｒｅｍovesillegalsentencesas
candidatesfiFomtherecognitionresult,theunderstandingpartalsorequiresagrammartofindthestructureof
alegalinputsentenceTherefbre，ourdialogsystemhastwodiflerentgrammarsfbrthedifIbrentaims・In
addition，ｔｈｅseparatedgrammarsareusefmfbrin‐dependenｔｄｅｖｅｌｏｐｍｅｎｔｏｆｔｈｅｐａｒｔｓｏｆｔｈｅｓｙｓteln
Figure2showsacontext-freegrammarfbrtherecognitionSystem・Ｔｈｅｐｏｓｉｔｉｏｎｏｆｅａｃｈｓｙｍｂｏｌｉｎａｐｒｏｄuc‐tionruleisrepresentedbyanorderednumber・An
orderednumberiscalculatedbyaddingthecolumn
andrownumbers・Storingthehistoryofruleapplica‐
tiｏｎｓａｓａｓｔｒｉｎｇｏｆｎｕｍｂｅｒｓ,thesystemcanpredictthe
nextwordefficientlybythetop-downprocedurelntherecognitionprocess，thepredictionofsuc-cessivewordsbysentencehypothesesandwordspottingareperformedinparallelfromthelefi-to-right・Foreachwordthathasbeenpredicte｡，ｔｈｅｗｏｒｄ

ＹＡＭＡＭＯＴＯｅｔａｌ：ＡＳＰＯＫＥＮＤＩＡＬＯＧＳＹＳＴＥＭＷＩＴＨＶＥＲＩＦＩＣＡＴＩＯＮ

8７

［、ｐｕｔ
－－－－－－－－－－－－－－－－－－－－－－－－－コ一一一一一一一一一一一一一一０0－￣:鯛！'｡，￣￣－－－－－－－

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Ｕｅｒｉｆｉ・Ｄｉａｌｏｇ

ＣａｌｌｉｎｇＣ１ａｒｉ・ｏｒ

SentencoComprehonsionil
Hｏｒｐｈｏｌｏｇｉｃａｌ
Ｐｌｎａｌｙｓｉｓ

立工ＨMH

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ＦｉｇｌＢｌｏｃｋｄｉａｇｒａｍｏｆｔｈｅsystem．

spottingscoreanditsbeginningpositionareobtainedusingtheViterbialgorithm・Weconstructedtheword
modelsbyconcatenatingsyllable-basedHMMsOur

０１

sentencelevelwithoutawordlatticeLikeword

239ruleswithoutdirectlyrewritingrulesfromword
classestoterminalsymbols，Ｔｈｅｖｏｃａｂｕｌａｒｙｓｉｚｅｉｓ４２６
ｗｏｒｄｓａｎｄｔｈｅｔaskperplexityis27・TheexperimentalＤexperimentallyshownthatourapproximatealgorithm
achievessentenceaccuracycomparabletooptimalalgorithms
ThesyntaxofthetasksentencesrelatedtoMt・
FUjisightseeingconsultationisdescribedbythe
context-fiFeegrammarthatisrepresentedbyrewritingｇａlgorithmissynchronizedfmmewise，itcaneasily
employthepruningmethodfbrsearchtrees・WehaveＦ

initialscorｅｏｆｔｈｅｗｏｒｄＳｉｎｃｅｔｈｅｓｅａｒｃｈｉｎｏｕｒ○１

spotting，ｉｔｕｓｅｓｏｎｌｙｔｈｅｈｉｇｈｅｓｔｏｆｔｈｅsentence
hypothesesscorestopredictthesamewordasthe

１２３４４Ｚ

８６４２０８algorithmincorporatesword-levelprocessingatthe＠s－－〉＠ＮＰ

２

３

@ＶＰ

＠s一＞＠ＮＰ*ＡＵＸ＠s－〉＊ＡＵＸＱＶＰ

＠ＮＰ－－＞＊ＤＥＴ

､ＮＰ２

＠ＶＰ

＠ＮＰ－－＞＠Ｗ２，ＮＰ２－－＞＊ＡＤＪ

QＮＰ２ＡｎｅｘａｍｐｌｅｏｆＣＦＧｆｌlbrspeechrecognitionpart．

resultshowsthesentencerecognitｉｏｎｒａｔｅｏｆａｂｏｕｔ７０

ｐｅｒｃｅｎｔｉｎｔｈｅｓｐeakeradaptationmode．
3.3SentenceComprehension

ForanalyzingmorphologyandJapanesebunsetsu(phrase)，possibleconcatenationsofwordsarerepresentedbyafinitestateautomatonKakari-Uke
relationships（dependencyrelationships）oftheJapaneselanguageareanalyzedbyKakari-Ukerules(dependencygrammar)．Astheresultofanalysis,theＩＥＩＣＥＴＲＡＮＳＩＮＦ．＆ＳＹＳＴ.，ＶＯＬＥ７６－Ｄ，Ｎｏ．ｌＪＡＮＵＡＲＹｌ９９３

8８(node-nameclass（slotI（ｓｌｏｔ２

node-name,）node-name2））

Fig3Syntaxofsemanticnetwork．

dependencystructureistransformedintoasemantic

networkbasedonthecasestructureofverbs、We

employselectionalrestrictiontodeterminewhetheraphrasecanorcannotmodifyanotherphrase・Ｅａｃｈｎｏｕｎｈａｓｔｈｒｅｅｋｉｎｄｓｏｆｓｅｍａｎｔｉcfbaturesthatare

determinedbythreedifIerentviewpoints・Ａｄetailed
discussionhasappearedinRef(2)．
AcharacteristicoftheJapanesespokenlanguage，
thedroppingofpostpositions(Japanesejoshi),causes
analyticaldifTicultywhenusingKakari-Ukerules・We
haveinvestigatedthisproblemandhaveshownthat
almostalldroppedpostpositionshavearequired-caseroleandwecanresolvedroppedpostpositionsusing
someheuristicsandsimplesemanticswithasuccessrateof90percent.(5)ＴｈｅｓｅｍａｎｔｉｃｎｅｔｗｏｒｋｆｂｒｍｉｓｓｈｏｗｎｉｎＦｉｇ３．
Ｎｏｄｅ－ｎａｍｅｉｓａｕｎｉｑｕｅｎａｍｅｔｏｒｅｐｒｅｓentanode
ClassrepresentsaconcepｔｏｆｔｈｅｎｏｄｅＴｈｅｎｏｄｅｉｓ
ｒｅｌａｔｅｄｔｏｎｏde-nameibyrelationsloti．Thesetof
posSibleslotsincludesthefbllowing：“form"，“whkind,,ａｎｄ“target”thatrepresentthesentencefbrm，suchasdeclarativesentence;“yes/no”ｏｒ"what"questions,ｉｎｃｌｕｄｉｎｇthetypeofinterrogativepronoun；ａｎｄｑｕｅｒｉｅｄｓｌｏｔｏｆ``what”ｏｒ“which”question,respective‐
lｙＴｈｅｆｏｌｌｏｗｉｎｇｉｓｔｈｅｓｅｍａｎｔｉｃｎｅｔｗＯｒｋｏｆｔｈｅ

ｓｅntence“Donokuraikakarimasuka?,,（anambiguous
question，whentranslatedintoEnglish，ｉｔｃａｎｍｅａｎ"Ｈｏｗｍｕｃｈｄｏｅｓｉｔｔａｋｅ？”ｏｒ“Howlongdoesit

take?，，)．

（n.take（fbrmwhat-question）（wh-kindhow-degree）（target?)）Inthisexample，“?，,ｍｅａｎｓｔｈａｔｔｈｅｖａｌｕｅｏｆｔｈｅｓｌｏｔ
ｃａｎｎotbeunderstood．“?，,isresolvedbythecontext
processingpart・However，whenthesystemcannot
resolveit,thesystemactivatestheclarihcationdialog
Weusethesemanticnetworkrepresentationwith‐ｏｕｔａｆｉｒｓｔｎｏｄｅ－ｎａｍｅｉｎｏｒｄｅｒｔｏｄenotethemeanings
insomesystemdatabases・Thisrepresentationwill
appearinthefollowingsections・Ｔｈｅｎｏｄｅ－ｎａｍｅｗｉｔｈ“$”representsthevariablethatcanmatchanynode‐
ｎａｍｅorsemanticnetworkForexample，“(gｏ（ｔｏ$dist))”meansthatsomeonegoessomewhere．
3.4ContextProcessing

Thecontextprocessingpartperformsthefbllowingtwoprocesses：hndingpronounantecedentsand

resolvingellipses．

3.4.1EllipsisResolutionbyDefaultValue

Thedomainofoursystemissightseeingguidance
forMtFuji、Sincethedomainisveryrestricted,itis
possibletoresolvesomeellipsesbydefnultvalues・Ｆｏｒ
example,ｔｈｃｕｓｅｒｉｎｐｕｔ“HowcanIgofromToyohashi?”isinterpolatedto“ＨｏｗｃａｎｌｇｏｔｏＭｔＦＵｊｉ
ｆＴｏｍＴｏｙｏｈａｓｈｉ？，，aslongasthesystemshowsexplicitlythedomaintotheuser・Thedetaileddiscussion
appearsinRef(2)．
3.4.2EllipsisResolutionbyContext
Oursystemstoresthesemanticnetworksofprevi‐
ouslyinputteddialogsentencesinthecontextstack
Ellipsesareresolvedbyreferencebetweenthesemantic
networkcurrentlybeingprocessedandthenetworksinthecontextstackConsistencyischeckedbycomparingallvaluesintheslots、ＩｆｎｏｓｌotcontainsdiHbrentvalues,twonetworksareconsistent・Theomittedslot

valueisobtainedffomaconsistentnetworkinthe
contextstack．

3.4.3ResolutionoftheTargetSlotofaQuestion

Ｉｆａｔａｒｇｅｔｓｌｏｔｎａｍｅｏｆａｑｕｅｓｔｉｏｎ,ｔｈａｔｉｓ，ａｖａｌｕｅ
ｏｆｔｈｅ“target，，slot，isomitted，thesystemcannot
answerthequestionForexample，thequestion
presentedpreviously“Donokuraikakarimasuka？,,isambiguousreferringtocostortimeOursysteminterpolatesitbyusingtwokindsofknowledge・ｏｎeistheverbslotdictionary，andtheotheristheinterrogative
pronoundictionary.(6)Ｔｈｅｖｅｒｂｓｌｏｔｄｉｃｔｉｏｎａｒｙｉｓａｓｅｔｏｆｌistsofaverb
andtheverb，sapplicableslotnamesForexample,theentryfbrtheverb“iku，，（go）ｉｓ“(ikuagentfromto
instrumentcosttiｍｅ)"、Theelementsintheparenthesisotherthan``iku,,areslotnamesthat“iku,,ｃａｎtake・Theinterrogativepronoundictionaryisasetoflistsof
interrogativepronounswithapplicableslotnames・Japanese“what,,questionsneedaninterrogativepronounandthepronounconstrainｓｔｈｅｔａｒｇｅｔｓｌｏｔｎａｍｅ
ｏｆｔｈｅｑｕｅｓｔｉｏｎ．Thelistrepresentsasetofcandiｄａｔｅｓ
ｆｏｒｔｈｅｔａｒｇｅｔｓｌｏｔｎａｍｅｏｆｔｈｅquestion、Ｆｏｒexample，
alistfbr“donokurai'，（ｉｎEnglish,“howmuch",“howlong，，orconcerningwithotherquantity）ｉｓ“(dono‐kuraiheightmasscosttime),,．
Thesystemcanobtaincandidatesfbｒｔａｒｇｅｔｓｌｏｔ
ｎａｍｅｓｂｙｕｓｉｎｇｔｈｅｄｉｃｔionaries・Ｔｈｅｉｎｔｅｒｓｅｃｔｉｏｎｏｆ
ｔｗｏｓｅｔｓｏｆｃａｎｄidateslilrtherrestrictsthem・Ifthereis

morethanonecandidate，thesystemactivatestheclarificationdialog．
3.4.4AnaphoraResolution
Thesystememploysverysimpleheuristicsfbr

ＹＡＭＡＭＯＴＯｅｔａｌ：ＡＳＰＯＫＥＮＤＩＡＬＯＧＳＹＳＴＥＭＷＩＴＨＶＥＲＩＦＩＣＡＴＩＯＮ

89

anaphoraresolution・Theantecedentofapronounis
decidedbyasearchofthecontextstack・Ｔｈｅｃｏｎｓｉｓ－

dialogrulesfbransweringtheuserquestioncan
includethe“ask，,instructionforaskingwhatthe

utteranceisregardedasanantecedentcandidate・The

ｄialogclarificationandverificationlnthcprocessing
oftheuserutterance,theinvocationofdialogrulesfbr
clarificationandverificationocＣｕrspriortonormal
dialogrules・Previousdialogsystemsmakequestions
fbrverificationbyaspecialmechanismfbreachpart．
Ｏursysteminvokesdialogrulesfbrverificationthat
areinterpretediｎｔｈｅｓａｍｅｗａｙａｓｎｏｒｍａｌｄｉａｌｏｇｒｕｌｅｓ・
Ｓｉｎｃｅｏｕｒｓｙｓｔｅｍｃａｎｕｓｅａｌｌｔｈｅｆｌａｃilitiesofthesystemforverificationandclarification,wecaneasilydevelop
complexrulesｆｂｒｔｈｅｍ．

ｔｅｎｔｎｏｄｅｉｎｔｈｅｓｅｍａｎticnetworkofthemostrecent

consistencyischeckedbycomparingsemanticfbaturesusedintheparser・Sincethissimplemethodoften
makesmistakes，thesystemactivatesaverification
dialogtoconfirmtheselection．

a5GeneratingtheNextUtterance
3.5．lOutline

Thenextutterancegenerationpartisinvokedafter
processingthecontextoftheinputsentence・Figure4
showstheHowofthispart・Iftheuserutteranceisa
question，ｔｈｉｓｐａｒｔｉｎｖｏｋｅｓａｎｅｗｄｉａｌｏｇｒｕｌｅｆbransweringthequestionandinterpretsit・Foradeclarativesentence,thepartexecutesanactivedialogruleon
therulestackTｈｅａｃｔｉｖｅｄｉａｌｏｇｒｕｌｅｉｓｔｈｅｔｏｐｒｕｌｅof
therulestack・TheuserinputispredictivelyinterpretedbyanactivedialogruleThemechanismcanbe
usedfbraninfbrmation-seekingtaskwhereausergives
thesystemtheinfbrmationthatneedstoberetrievedfi｢omadatabaＳｅａｎｄｔｈｅｓｙｓｔｅｍｇｉｖｅｓｔｈｅｕｓｅｒｔｈｅ
ｉnfbrmationthats/ｈｅwants・However,therulesand
databasesdevelopedinthefbllowingsectionsare，ｏｆ
Course，specifictosimulatingtheMtFujitravelbureauAlthoughthereissomeresearchthatusesrule
setsfbrdecidingthenextutterance，ourmethodis
difIerent丘omthese(7)regardingtheinvocationmechaｎｉｓｍｏｆｎｅｗｄｉａｌｏｇｒｕｌｅｓａｎｄｔｈｅｓｅｔｏfinstructions
Sinceinoursystemtheuserqueryinvokesthedialogrules，ｗｅｃａｎｅａｓｉｌｙｄｅｖｅｌｏｐｔｈｅｒｕｌｅｓｆｂｒｔhecomplexquestion-answerdialｏｇＦｏｒｅｘａｍｐｌｅ，ｔｈｅＳｅｍａｎｔｉｃＮｅｔＩＡｌｏ「ｋsystemneeds・Ａｌｓｏｏｕｒｓｙｓｔｅｍｈａｓａｍｅｃｈａｎｉｓｍｆｂｒ

3.5.2DialogRulesandDatabasefbrSentenceGen●

eratlon

Ａｄｉａｌｏｇｒｕｌｅｉｓａｓｅｔｏｆｉｎｓｔｒｕｃｔｉｏｎｓ・Figure5

showsthefbrmandinstructionsofadialogruleEach
instructionisinterpretedindownwardorder・Interpretationistenninatedatthegenerationofinterrogative
orlongassertivesentences・Thefbllowingareexplanationsforeachinstruction

IfMostinstructioninadialogrulebelｏｎｇｔｏｔｈｉｓ
ｔｙｐｅ、Ａｎ“i「，instructionconsistsofaconditionand

instructions・Ｉｆｔｈｅｃｏｎｄｉｔｉｏｎｉｓｔｒｕｅ，instructionsare

interpretedE1ementsintheinstructionpartof“iF,are
instructionsfbrthedialogmle・Ｏｆｃｏｕｒｓｅｗｅｃａｎｕｓｅ"i「，instructionsrecursively・Ｆortheconditionalpart，
twopredicatesareprepared・Ｔｈｅ“be1,,predicatetakes
asemanticnetworkａｓａｎａｒｇｕｍｅｎｔａｎｄｒｅｔｕｒｎｓｔｒｕｅ
ｗｈｅｎｔｈｅｒｅｉｓａｃｏｎｓｉｓｔｅｎｔｎｅｔｗｏｒｋｔｏｔｈｅargumentin
thecontextstackThe“input-now，，ｐｒｅｄｉｃａｔｅｉｓｔｈｅｓａｍｅａｓ“be1,,butmatchestheprevioususerinputｏｎｌｙ，
Alsoゥlogicaloperatorssuchas``and,，，“or,，ａｎｄ“not，，
Ｓｙｎｔａｘｏｆｄｉａｌｏｇｒｕｌｅ：（ｄｅｆｒｕｌｅｒｕｌｅ－ｎａｍｅｉｎｓｔｒｕｃｔｉｏｎ１

Ｙ
Ｑｕｅｓｔｉｏｎ？

ｉｎｓｔｒｕｃｔｉｏｎ２）

ｌｎｖｏｃａｔｉｏｎｏ７
ＮｅＨＤｉ
ＮｅＨＤｉａｌｏｇＲｕｌｅ

Ｎ

Ｒｕｌｅ

Ｉｎｓｔｒｕｃｔｉｏｎ：（ifconditioninstructionlinstrucｔｉｏｎ２．．．）（tellSEM-net）（ｔｅｌｌ２ｓｔｒｉｎｇ）（asktypeSEN-net）●

ｌｎｔｅ「
ｌｎｔｅ「ｐｒｅｔｅｒ（callrule-name）（ｅｉｔｈｅｒ（conditionlinstruction,）（condition2instruction2））(return）(s-return）

Ｕｔｔｅ「？

ＮＹ

ＯｕｔｐｕｔＦｉｇ４Ｆｌｏｗｆｂｒｇｅｎｅｒａｔｉｎｇｔｈｅnextutterance

Ｃｏｎｄｉｔｉｏｎ：（belSEH-net）（input-nowSEN-net）

Fig5Syntaxandinstructionsofdialogrule．

1ＥＩＣＥＴＲＡＮＳ・ＩＮＦ．＆ＳＹＳＴ.，ＶＯＬＥ７６－Ｄ，Ｎｏ．ｌＪＡＮＵＡＲＹ１９９３
9０

ＦｒｏｍｔｈｅｒｅｙｏｕｃａｎｕｓｅＲｏｕｔｅｌ３８．'．）(defrulehow-to-go（iｆ（not（bｅｌ（gｏ（from＄sour)）））（askｗｈａｔ（gｏ（ｆｒｏｍ＄sour)）））（iｆ（not（bｅｌ（gｏ（ｂｙ＄inst)）））（askｗｈａｔ（gｏ（ｂｙ＄inst)）））（tｅｌｌ（gｏ（way＄x)）））Ｆｉｇ６ＡｎｅｘａｍｐｌｅｏｆＴｅｌｌｄａｔａｂａｓｅ．

Ｆｉｇ７Ａｎｅｘａｍｐｌｅｏｆｄｉａｌｏｇｒｕｌｅ．

(ｇｏｗａｙｃｏｎｓｔ：（fromToyohashi）（toMt・Ｆｕｊｉ）（ｂｙcar）（9Ｊ（toGotenba）（way＄x)）：,'ＹｏｕｃａｎｕｓｅＲｏｕｔｅｌ３８ｆｒｏｍＧｏｔｅｎｂａ．，,

ｎｉｌ：”ＧｏｔｏＯｏｔｅｎｂａｉｎｔｅｒｃｈａｎｇｅｂｙＴｏｕｍｅｉｈｉｇｈｗａｙ・

Tell：Ａ“tell，，instructiontakesasemanticnetworkas（gｏ（way（bow-to-go)）（instrument（ｒｕｌｅi））〉

anargumentandgeneratesasystemutteranceThe

Fig8Anentryofruledatabase．

ｃａｎｂｅｕｓｅｄｉｎｔｈｅｃｏｎｄｉｔｉｏｎａｌｐａｒｔ・semanticnetworkmustincludeavariablewhichrefiers
tothecontentoftheutterance・Thesentencesthem-selvesarestoredinthespecialdatabasefbr“tell，，
instructions（telldatabase)．Thetelldatabaseincludes

almostallinformationthattheuserwants・Theinter-

polatedargumentbycontextisusedasasearchpatternfbrthetelldatabase・Slotswithoutvariablesare
constraintsfbrthesearch

Sincesentencesforexplanationdependontheuser

knowledgelevel，sentencesinthetelldatabasehave

otherconditionsbasedonthisknowledgeleveLThe
usｅｒｋｎｏｗｌｅｄｇｅｌｅｖｅｌｉｓｋｎｏｗｎａｓｔｈｅｕｓermodeL
AnexampleofthetelldatabaｓｅｉｓｓｈｏｗｎｉｎＦｉｇ
６．Ｔｈｅｆｉｒｓｔｌｉｎｅｓｈｏｗｓｔｈａｔｔｈｉｓｅｎｔｒｙｉｓｄａｔａｆｏｒｔｈｅｒｏｕｔｅｏｆａｔｒｉｐ・Thesecondlinerepresentsthecon‐straintsfbrtheusageThefbllowinglinesaresentences
fbrtheexplanａｔｉｏｎｏｆｔｈｅｒｏｕｔｅｏｆｔｈｅｔｒｉｐ・The
semanticnetworkandnilareuseｒｋｎｏｗｌｅｄｇｅｃｏｎ‐straintslftheuserknowsavalueofthevariableinthe

network,thenextsentencecanbeusedfortheexplanation．“Nil，，representsnoconstraint・
Inamoregeneraldialogsystem，thisdatabasecorrespondstothedatabaseofanswersinthesystem,sdomainTofacilitatemodularity，thedatabasesof
answersanddialogcontrolinstructionsshouldbe

separated.(8)However,ｔｈｉｓｉｓｓｕｅｉｓｎｏｔｉｍｐｏｒｔａｎｔｆｏｒ
ｏｕｒｐurposes

Tell2：Ａ“tell2,,instructiontakesastringasthe
argumentandgeneratesitdirectlyasthenextsystemutterance・

tionlnthiscase,thesystemactivatestheclarihcation
dialog・Return，s-return：Ａｄｉａｌｏｇｒｕｌｅｉｓｔｅｒｍｉｎａｔｅｄａｎｄ
ｔｈｅｐreviousruleispoppedfromtherulestackThepoppedruleisexecutedcontinuously．‘`Return，，ｐｏｐｓ
ｔｈｅｒｕｌｅｓｔａｃｋｏｎｌｙ・Ｂｕｔ“s-return,，alsopopsthesystem

stack．“S-return，，ｉｓｕsedfbrtheverificationandclarificationdialogrule、

ＡｎｅｘａｍｐｌｅｏｆａｄｉａｌｏｇｒｕｌｅｉｓｓｈｏｗｎｉnFig7
Thisruleisusedfbrteachingｈｏｗｔｏｇｏ・Ifthesystemdoesnotknowthedeparturepointorthemeansof
transport,theinterpreterexecutesan“ask,,instruction
andgeneratesaquestiｏｎＡｆｔｅｒｔｈｅｕｓｅｒｒｅｓponseto
anyquestions，ｔｈｅｓｙｓｔｅｍｇｅｎｅｒａｔｅｓａｎａｎｓｗｅｒｂｙｔｈｅ"tell”instruction

AdialogruleisinvokedbyuserquestioningTheruledatabasestorestherulenameaccesseｄｂｙｔｈｅｓｅｍａｎｔｉｃｎｅｔｗｏｒｋｏｆｔｈｅｕｓｅｒquestion、Thesemantic
networkofa``what,，questionhasa“?”asaslotvalue・

Ａｎｉｎｖｏｋｅｄｒｕｌｅｉssearchedintheruledatabasebythｅｃｌａｓｓｎａｍｅｏｆｔｈｅｎｅｔｗｏｒｋａｎｄｔｈｅｓｌｏｔｎａｍｅｗｉｔｈ“?，，、Ｆｉｇｕｒｅ８ｓｈｏｗｓａｎｅｎｔｒｙｉｎｔｈｅｒｕｌｅｄａtabase、Ｔｈｅｒｕｌｅｏｆ“how-to-go，，ｉｓｉｎｖｏｋｅｄｂｙｔｈｅｎｅｔｗｏｒｋ（noｇｏ（fbrmwhat-question）（wh-kindhow-method）（targetway)）’

Ask：Ａ、“ask,，instructionisusedfbrgenerating
questionsbythesystem・Ittakestwoarguments・Ｏｎｅ
ｉｓｔｈｅｓａｍｅａｓｔｈｅａｒｇｕｍｅｎｔｏｆ“tell"・Theotherrepresentsthetypeofquestionsuchas“yes/no,,，ofthesentence“douyatteikunodesuka?”（"Ｈｏｗｄｏｌ
ｇｏ?,,）andtheruledatabase．

instructionssimilartothetelldatabase・Anoutput
sentenceisaquestionseekingavalueofavariableinthesemanticnetworkMoreover,thesemanticnetwork
isstoredinthepredictedtemplateforcontextprocess‐

Thespeechgenerationpartgeneratesavoiced
utterance、Sinceourresearchconcentratesonspeech
understandinganddialog，weimplementasimple
mechanismusingtemplatesWehavepreparedsome
PCMrecordingtemplatesforthesystem,srepresentativesentences・Thesysteminterchangessomewordsin
atemplatefbrgeneratingdifIbrentsentencesfromthe

3.6Generation“what"，ｏｒ‘`which，,、Thereisadatabasefbr‘`ask”

1ｎｇ

Ｃａｌｌ：Invocationofanewdialogrule・
Either：Ａｓｅｔｏｆｐａｉｒｓｏｆｃｏｎｄｉｔｉｏｎｓａｎｄｉｎｓｔructions、
Iftheconditionistrue，pairinginstructionsareinterpretedAlltheconditionsareevaluａｔｅｄａｔｔｈｅｓａｍｅ

ｔｉｍｅ・ＷｈｅｎｍａｎｙｃｏｎｄｉｔｉｏｎｓａｒｅｔｒｕｅｏｒａlltheconditionsareMse,thesystemcannotdecideonaninstruc-representatlvesentences．

ＹＡＭＡＭＯＴＯｅｔａｌ：ＡＳＰＯＫＥＮＤＩＡＬＯＧＳＹＳＴＥＭＷＩＴＨＶＥＲＩＦＩＣＡＴＩＯＮ
９１

3.7InvocationoftheVerificationandClarification

DialogWhenambiguitiesoruncertaintiesthatcannotbe

resolvedwithinthesystemoccur，thesystemactivates
theverificationorclarificationdialog・Thepartwhere
theambiguitiesexistdictatesthedialogruleandcalls
thepartinvokingtheverificationandclarification

dialogThatpartpushesthecurrentprocessingstatus
intothesystemstackandinvokesthewholedialog
systemusingthedecidedrules・Returningfromthe
clarificationdialogtｏｔｈｅｐｒｅｖｉｏｕｓｏｎｅｉｓａｃｈｉｅｖｅｄｂｙ
ａｎ“s-return,，ｉｎstructionTherestartedpartcanuse

theinfbrmationreceivedfiFomtheverificationor

clarificationdialog．

4．VerificationandClarificationDiａｌｏｇｆＯｒＥａｃｈ
ＰａｒｔｏｆｔｈｅＤｉａＩｏｇSystem
4．lAmbiguitiesintheDialogSystem

Evenahumanasksquestionstoverifyinfbrmationwhens/hecannotresolveambiguitiesinanotherperson,sutterance・sincemachineshavealowerabil-

ityforresolvingambiguitythanhumans，itisimportanttoaggressivelyaskverificationquestions
Ahumanprocessesanutterancebyaunified
mechanismconsistingoffbrexample,speechrecognition，morphologyanalysis，andparsingAlthough
therearestudiestodevelopaunifiedmechanismfbr
machineprocessing,(9)weemployedseparatedmechanismstoprocessefIicientlyspokensentencesanddialog
usingcurrenttechniques・Ｔｈｅｐａrtsofseparated
mechanismsystemsareisolatedandconnectedbyvery
narrowpipes・Ahumancanresolvemanyambiguities
inspokennaturallanguagebasedonwholekindsof
knoWledge，which，incomparison，amachinecannot
do・Ｔｈｅｐａｒｔｏｆａｍａｃｈｉｎｅｄｉａｌｏｇｓｙｓｔｅｍｍustresolve
anambiguityusingknowledgecontainedwithinthat
partandtheresultsfiBomaflewotherparts・Ifthepartneedsmoreinfbrmationffomotherparts，adead-lockproblemwhereallpartsmustwaitfbrtheresultsof
otherpartsmayoccur・However,thesystemcanavoid
thissituationbyusingaclarificationorverification
query・

Ｔherearemanymethodsfbrtheclarificationand

verificationincompliancewiththeprocessingineachpart・Inthissectiontheyarediscussed
4.2SpeechRecognition

Ｉｔｉｓｗｅｌｌｋｎｏｗｎｔｈａｔｓｐｅｅｃｈｒｅｃｏｇｎｉtionisavery
diHiculttaskThereasonisthataspeechsignalcon‐
sistsofmanyindeterminaciessuchasvarietiesof
speechpatterndependingonspeakerdiflbrences，concatenation，ordurationofutterance，ambiguitiesof

segmentation,interjections,andnoise・Tocopewith

theseindeterminacies，stochasticmethodshavebeen

employedinarecentautomaticspeechrecognition
system.('o）Ｔｈｅｒｅsultofrecognitionbystochastic
mｅｔｈｏｄｓｉｓａｓｅｔｏｆｃａｎｄｉｄａｔｅｓｔｒｉｎｇｓwithrespective
probabilities・Ifthemaximumprobabilityamongthe
candidatestringsislowerthanathreshold，thesystemcannotdetermineacorreｃｔａｎｓｗｅｒ、Intheclarificationdialogfbrthespeechrecogni-

tionpart,itisimportanttodecidewhattoaskａｎｄｈｏｗ
ｔｏａｓｋｉｔ・Ａｌｔｈｏｕｇｈｓｏｍｅｓｔｕｄｉｅｓｈａｖｅｂｅｅｎｃｏｎducted

onthissuhject,(u)therehasnotyetbeenanyinvestigationintoadialogsystembasedonmodernspeech
recognitiontechnology・Thesimplestmethodofclarificatiｏｎｉｓｔｏｐｒｏｍｐｔ
ｔｈｅｕｓｅｒｔｏｒｅｐｅａｔｔｈｅsamesentencebyoutputting"wakarimasen”（ｉｎEnglish“don,tunderstand，,)．

However,thismaybetheleasteHicientmethodof

clarificationAsstatedintheSect・Ｚ,humansseldom

asktorepeatcompletesentences・Ｔｈｅｓｙｓｔｅｍｓｈｏｕｌｄｂｅ
ａｂｌｅｔｏａｓｋｔｏｃｌarifyonlytheambiguoUspartofa
sentence・Therefbreonlyifthesystemobtainｓｎｏ
ｃａｎｄｉｄａｔｅｓｆｉＦｏｍｔｈｅｉｎｐｕｔｓｅntenceorcandidateswith
verylowprobabilities，ｄｏｅｓｔｈｅｓｙｓｔｅｍｓａｙ“Ｉｄｏｎ，tunderstand，,．Otherwise，ｏｕｒｓｙｓｔｅｍｓｈｏｗｓｓｏｍｅｐｏｓ‐
siblecandidatestotheuserandrequeststheuserto
selecttheappropriatecandidate．
4.3SentenceComprehension
Thesentencecomprehensionpartconsistsofthree
processes：morphologicalanalysis，parsing，andtransformationintoasemanticnetworkAlthoughambiguitiescanoccurineachsubpart,thesystemshouldnot
askaquestionａｓｓｏｏｎａｓｉｔｏｃｃｕｒｓ，becausemany
ambiguitiescanberesolvedbythecooperationofthreeparts、Ｆｏｒexample，thesegmentationalambiguityof

themorphologicalanalysissubpartcanberesolvedby
parsingwithsemanticfieaturesltisalsodifTiculttoask
theusertoclarifysegmentationalambiguityusing
naturallanguage、Thesystemleavesambiguitiesofthethreesub－
partsaloneuntilobtainingasemanticnetworkfrom

thesentencecomprehensionpart・Ｔｈｅｒｅｓｕｌｔｉｓａｓｅｔｏｆ
ｓｅｍａｎｔｉｃｎｅｔｗｏrksascandidates・Ｉｆｔｈｅｓｅｔｈａｓｍｏｒｅ

ｔｈａｎｏｎｅｃａｎｄｉdate,thesystemactivatesaclarification
orverificationdialoglnoursystemthequestionfbr(defruleverification-network（askyes/ｎｏ＄sem-net）（iｆ（oｒ（input-now（exc-yes)）（input-now（exc-no)））（s-return)）（ｔｅｌｌ２，.ｈａｉｍａｔａｈａｉｉｅｄｅｏｋｏｔａｅｋｕｄａｓａｉ．”））（.'ＰｌｅａｓｅａｎｓＷｅｒｙｅｓｏｒｎｏ.”）Fig9Adialogrulefbrverification．

ＩＥＩＣＥＴＲＡＮＳ、ＩＮＦ．＆ＳＹＳＴ.，ＶＯＬＥ７６－Ｄ，Ｎｏ．ｌＪＡＮＵＡＲＹ１９９３
9２

verificationisgeneratedfromasemanticnetworkbya
specialtemplateforsemanticnetworkpatterns，Ｆｉｇｕｒｅ９ｓｈｏｗｓｔｈｅｄｉａｌｏｇｒｕｌｅｆｏｒｔｈeverification
ofacandidatewithalowprobability．“$sem-net”isreplacedbythccandidatesemanticnetwork．（Exqyes）and（exc-no）aresemanticnetworksof"yes，,ａｎｄ"ｎｏ,,，respectively・Ｔｈｉｓｒｕｌｅｉｓａｃｔｉｖｅｕｎｔｉｌｔｈｅｕｓｅｒｕｔters“yes,,ｏｒ“ｎｏ'，．

4.4ContextProcessing

Ellipticalphrasesareinterpolatedbythecontext
stackandtemporaltemplate・Inmanycases，dialog

rulesuse“ask，，instructionstoobtaininfbrmationfbr

answeringtheuser，soriginalquestionTheinforma‐

ｔｉｏｎｆｒｏｍｔｈｅｕｓｅｒｉｓｓｔｏｒｅｄｉｎｔｈｅｃｏntextstackThis

isusedtoresolveellipseswhenoursystemanswersthe
originaluserquestion・However,sinceourinterpolat‐
ingmethodisverysimple,ｔｈｅsystemsometimesmakes
amistake・Oursystemverifiesinterpolatedsentences

befbreansweringtheuser,soriginalquestion
Sinceananaphoraresolutionresultisalsouncer‐
taininoursystem,thesystemmustverifywhetheritis
correctornot・Recognizingpossiblemistakes，thesystempromptstheusertoutterasentencewithout
us1ngpronouns．

4.5Rulelnvocation

Ｔｈｅ“either”ｉｎstructionfordialogrulesisused
forthesystemtochoosetheappropriatedialogrulefromalternatives・Eachcandidatedialogrulehasa
conｄｉｔｉｏｎ、TherulewithatrueconditionisactivatedIftwoormorerulesareactivatedorallconditionsare

false,thesystemcannotdecidethenextactivatingrule
Anambiguityofruleselectionoccurs・
Togenerateaquestionfortheselection,eachrulehasasentencethatexpressestherule,ｓｐｕｒｐｏｓｅＴｈｅｓｙｓｔｅｍｓｈｏｗｓｔｈｅｕｓｅｒａlltheruleexplanationsandthe
userselectstheappropriaterule．

sys：Ｆｕｊｉ－ｓａｎｋａｎｋｏｕａｎｎａｉ－ｊｏｄｅｓｕ．

（ＴｈｉｓｉｓｔｈｅＨｔ､Ｆｕｊｉｔｒａｖｅｌｂｕｒｅａｕ.）

usr：Ｆｕｊｉ－ｓａｎ－ｅｉｋｉｔａｉ．
（Ｉ,ｄｌｉｋｅｔｏｇｏｔｏＭｔ・Ｆｕｊｉ.）

ｓｙｓ：Ｆｕｊｉ－ｓａｎ－ｎｏｋａｎｋｏｕｃｈｉ－ｎｉｔｓｕｉｔｅｓｅｔｓｕｍｅｉ
ｓｈｉｍａｓｕｋａ？[4.5］soretomqFuji-san-enoikikata-wｏ
ｓｅｔｓｕｍｅｉｓｈｉｎａｓｕｋａ？

（Ｓｈａｌｌlexplamsightseeingspotsnear

Ht､ＦｕｊｉｏｒｔｈｅｗａｙｔｏＮｔ・Ｆｕｊｉ？）
usr：douyatteFuji-san-madeikemasuka？
（ＨｏｗｃａｎｌｇｏｔｏＭｔ・Ｆｕｊｉ？）
sys：dokokarakimasuka？［3.5.2］（Ｆｒｏｍｗｈｉｃｈｃｉｔｙ?）
ｕｓｒ：Ｔｏｙｏｈａｓｈｉ－ｋａｒａｉｋｉｍａｓｕ．

（Ｉ,llgofromToyohashi．）
ｓｙｓ：ｋｕｒｕｍａ－ｄｅｋｉｍａｓｕｋａ？ｄｅｎｓｈａ－ｄｅｋｉｍａｓｕｋａ？
［3.5.2］（Ａｒｅｙｏｕｃｏｍｉｎｇｂｙｃａｒｏｒｔｒａｉｎ?）

ｕｓｒ：ｓｏｋｏｍａｄｅｋｕｒｕｍａ－ｄｅｉｋｉｍａｓｕ．

（Ｉ,ｌｌｇｏｔｈｅｒｅｂｙｃａｒ.）ｓｙｓ：”soko',towaFuji-san-nokotodesuka？［4.4］（Does”there”ｒｅｆｅｒｔｏＭｔ・Ｆｕｊｉ？）

ｕｓｒ：ｈａｉ．

（Yes.）
ｓｙｓ：Ｔｏｙｏｈａｓｈｉ－ｋａｒａＦｕｊｉ－ｓａｎ－ｍａｄｅｋｕｒｕｍa-de

ikuhouhou-wootazunedesuka？［4.4］（Ａｒｅｙｏｕａｓｋｉｎｇｂｏｗｔｏｇｏｔｏｌｖｌｔ・Ｆｕｊｉｆｒｏｍ
Ｔｏｙｏｈａｓｈｉｂｙｃａｒ?）ｕｓｒ：ｈａｉ．

（Yes.）
ｓｙｓ：Ｇｏｔｅｎｂａ－ｍａｄｅｎｏｉｋｉｋａｔａ－ｗｏｓｈｉｔｔｅoraremasuka？［4.6］（ＤｏｙｏｕｋｎｏｗｈｏｗｔｏｇｅｔｔｏＧｏｔｅｎｂａＣｉｔｙ?）

ｕｓｒ：ｉｉｅ．

（No.）

ｓｙｓ：Ｔｏｕｍｅｉｋｏｕｓｏｋｕ－ｄｅＧｏｔｅｎｂａｉｎｔａ－ｍａｄｅｋｉｔｅ，
sokokaral38gousen-deikemasu．［3.5.2］（GotoGotenbainterchangebyToumei

expressway，ｕｓｅＲｏｕｔｅｌ３８ｆｒｏｍｔｈｅｒｅ.）

ｕｓｒ：ｄｏｒｅｋｕｒａｉｋａｋａｒｉｍａｓｕｋａ？

（Howmuchdoesittake?）ｏｒ（Ｈｏｗｌｏｎｇｄｏｅｓｉｔｔａｋｅ?）ｓｙｓ：jikan-gadesuka？okane-gadesuka？［3.4.3］（Ｄｏｙｏｕｍｅａｎｔｉｍｅｏｒｍｏｎｅｙ?）

ＦｉｇｌＯＥｘａｍｐｌｅｏｆｄｉａｌｏｇ．

4.6UserModel

WedescribedtheuserknowledgelevｅｌｉｎＳｅｃｔ、
3.5.2．Explanationsentencesthathavethｅｓａｍｅｍｅａｎ‐
ingsshouldbevariedaccordingtotheuserknowledge
leveLInresponseｔｏｔｈｅｓａｍｅｉｎｐｕｔｑｕｅｓｔｉｏｎ，thesystemmustrespondwithananswermoreeasilyunderstoodbyauserwithlowerlevelknowledgeaboｕｔｔｈｅｄｏｍａｉｎｔｈａｎｂｙａｎｕｐｐｅｒｌｅｖｅｌｕser.('2）
Thetelldatabasehasasentencewithacondition

concerningtheuser，sknowledgeleveLIfthereare
alternativeanswers，thesystemcanconfirmtheuser,sknowledgeleveldirectly．

5．ＡｎＥｘａｍｐｌｅｏｆＤｉａｌｏｇ

ＦｉｇｕｒｅｌＯｓｈｏｗｓａｎｅｘａｍｐｌｅｏｆａｄｉａｌｏｇｂｅｔｗｅｅｎａ
ｈｕｍａｎｕｓｅｒａｎｄｏｕｒｄｉａｌｏｇsystem．“Usr，，ａｎｄ“sys,，
indicatetheuserandsystemutterances，respectively・
ＴｈｅａｃｔｕａｌｄｉａｌｏｇｉｓｃａｒｒｉｅｄｏｕｔｉｎｓpokenJapanese

SentencesintheparenthesesaretheEnglishtransla‐

tionsThenumberinbracketsfbllowingsystemutter‐
ancesshowsthesectioninthispaperwherethegenerationmechanismisdescribed．

6．TheCurrentStateoftheSystemｌｎＳｅｃｔ､２，wecategorizedclarihcationand

verificationqueries・Thecategoriesofquericsthatthe

ＹＡＭＡＭＯＴＯｅｔａｌ：ＡＳＰＯＫＥＮＤＩＡＬＯＧＳＹＳＴＥＭＷＩＴＨＶＥＲＩＦＩＣＡＴＩＯＮ

9３

currentsystemcangenerateare(1)，(3)，（５）ａｎｄ（６）in
Sect２．２．Theknowledgeforgeneratingthequeriesof(1)，infbrrnationseekingtoanswerquestions,isstoredindialogrulesassequencesofinstructions（seeSect､３．

５)．Thequeriesof(3)，decidingthelevelofanswer，
aregeneratedbyexecutinga“tell，,instructionwith
referencetodialogrules（seeSects､３．５．２ａｎｄ４．６)．If

thecurrentsystemcannotobtainaresultffomthe

speechrecognitio、part,parseasentenceortransform
itintosemanticnetworks，itgeneratesasentenceindicatingthatitcannotunderstandtheuserinput・This
correspondstocategory(5)，representationofincomprehenｓｉｏｎ・TheotherprocessesinSect4generatequeriesfbrverificationofsentencemeaningscorre‐
spondingtocategory(6)．
Thequeriesofcategory（７）repeatpartoforthe
entireprevioussentenceFromtheviewpointof
verificationofsentencemeaningcategory（７）ｉｓｉｎ‐cludedincategory(6)．However，category（７）isconcernedwiththesurfacesoundsofsentences、The

Ｔｏｏmanyverificationandclarificationqueriestendto
irritatetheuser・Thereadermightthinkthatthe

disadvantageSoftoomanyqueriesoutweightheadvantagesofanaturallanguageinterfaceHowever，theuser，ｓｆＴｅｅｄｏｍｆｏｒｉｎｐｕｔａｎｄｔｈｅｌａｃｋｏｆｐｒiorlearning
requirements，whichareimportantadvantagesofa
naturallanguageinterface，areretainedasfieaturesof
oursystemwithclarificationandverification
Ourdialogsystemformsmanyquestionsthat
requireonlya‘`yes，，ｏｒ“ｎｏ，，response,whichiseasily
recOgnizablebythecurrentspeechrecognitionmechanism・Ｔｈｕｓｉｔｉｓｉｍｐｏｒｔａｎｔｔｏｓｔｕｄｙｗａｙｓｏｆｑｕｅｒｙｉｎｇ
ｗｈｉｃｈｌｉｍｉｔｔｈｅｕｓｅｒ，sresponse
7．Conclusions

Ｗｅｈａｖｅｄｅｓｃｒｉｂｅｄａｓｐｏｋｅｎｄｉａｌｏｇｓｙstemthat
cangenerateverificationandclarificationqueries・In
human-humandialog，tenpercentofallsentencescollectedareconcernedwithverificationor

systemcannotsimulatedialogsexactlylikecategory(7)，becauseithastorepeatthesamesurfacesound，
ｎｏｔｔｈｅｓａｍｅｍｅａｎｉｎｇＴｈｅｓｙｓｔｅｍｄｏｅｓnottake
accountofthat，ａｌｔｈｏｕｇｈｔｈｅｓａｍｅｓｕｒｆａｃｅｓｏｕｎｄｉｓ
ｇeneratedsometimesbychanceThemechanismfbr
generatingqueriesof（２），refinementofmeaning，
requiresamethodfbrdecidingwhatｔｈｅsystemasks
fmmtheinputsentence・Ｗｅｎｅｅｄｍｏｒｅｓｔｕｄｉｅｓｏｆｔｈｅ
ｍｅｔｈｏｄｏｆdecidingthedirectionofrefinement・We
willcontinuestudyinthisfieldusingoursystemTheclarification・Inhuman-machinedialog，ｉｔｉｓｅｖｅｎｍｏｒｅｉｍｐｏｒｔａｎｔｔｈａｔｔｈｅｍａchineverihesambiguities
thatoccurinprocessingadialogInparticular,ｓｉｎｃｅ

generatedusingasimplespeechrecognitionmecha‐
ｎｉｓｍｕｎａｂｌｅｔｏｃｏｐｅｗｉｔｈｕｎｋｎｏｗｎｗｏｒds
Sincewedidnotexperimentallyevaluatethe

verificationandclarificationmethodstoflacilitatemore
naturalman-machinecommunication．

queriesof(4)ａｎｄ(8),clarificationofwordmeaning
andverihcationofsentencepartsounds，cannotbe

efT1ectivenessofourclarihcationandverificationmech-anism,wecannotclearlyjudgeitseflbctiveness・How‐
ever，ｗｅｃａｎｓｔａｔｅｗｉｔｈｃｅｒｔａｉｎｔｙｔｈａｔｗｅｃaneasily
developasystemwithclarificationandverificationfbr
allparts，ｕｓｉｎｇｏｕｒｆｒａｍｅｗｏｒｋ
Ｔｈｅｒｅａｒｅｍａｎｙｓtudiesregardingtherefinementof
sentencecomprehensionabilityIfpowerfilltechniquessuchastheplan-basedmechanismfbrinfierence
ofuserintentionareusedandthesystemmakesa
correctinflerence，itisagreatadvantagefbrtheuser・
However，ifthesystemmisunderstandsasentence,thesubsequentdialogmaycollapseｏｒｔｈｅｓｙｓｔｅｍｍａｙ
ｇｅｎｅｒａｔｅａｎａｎｓｗｅｒｎｏｔｉｎｔｅｎｄｅｄｂｙｔｈｅｕｓｅｍＩｆｔｈｅｃｏｎｆidenceinresultscanbemeasured,wecandevelOp
abettersystemthatemployspowerMtechniquesandclarificationandverificationmethods・Unfortunately，
itisdifTiculttomeasuretheconfidenceinresultswhen

usingsuchpowerfUltechniques
Althoughwedonotｉｎｔｅｎｄｆｂｒｔｈｅｓｙｓｔｅｍｔｏ
ｓｉｍｕｌａｔｅａｈumandialogandwehavedevelopedthe
systemthatcanmakeman-machinedialogmorereli-able,ｉｔｉｓ,ｏｆcourse,importantthatitbeuser-friendly．

ｓｐｅｅｃｈｓｉｇｎａｌｓｈａｖｃｍａｎｙｕｎｃｅrtainties，theclarificationdialogisveryimportanL

TheverificationandclarihcationfUnctionsofour

spokendialogsystemareimplementedusingaUfacilitiesofthedialogsystemrecursively．Ｗｅｈａｖｅ
ｃｏｎｈｒｍｅｄｔｈａｔｏｕｒｓｙｓｔｅｍｉｓｍorerobustthansystems
havingnoverificationorpoorverificationandclarihcationfnnctions・Ourcurrentresearchdirectionincludesextending

Acknowledgements

TheauthorswishtothankMr・MasatoyoTaguchi，whodevelopedanearlierversionofthedialogsystem，
andMr・AtsuhikoKai,whodevelopedthebasicspeech
reCognitionsystenL
Weusedtranscriptsinthecontinuousspeech
corpuSfbrresearchoftheAcousticalSocietyofJapan．
ＷｅｗｉｓｈｔｏｔｈａｎｋａｌｌｍｅｍｂｅｒｓｏｆｔｈｅＣｏｍｍｉｔｔｅｅｏｎ

ＣｏｎｔｉｎｕｏｕｓＳｐeechDatabaseoftheAcousticalSocietyofJapan．

References（１）edBrady,ＭａｎｄＢｅｒｗｉｃｋＲ.Ｃ,Ｃ川〃rα伽'、/ＭＯｄｂＺＦ
ｑ/Discou応aMITPress,1983.
（２）Nakagawa,Ｓ,Takemoto,SandTaguchi,Ｍ,“Translationfi｢omJapanesesentencetofirstorderpredicatecalculusinquestion-answenngsystemfbrtrafficregulaｔｉｏｎ
ＬＩＣＥＮＣＥ,，，刀α"３．ノＰＳＺｖｏＬ３２，ｎｏ､３，ｐｐ.354-363,
1991.

（３）Niedermair,ＧＴ.,Streit,MandTropf;Ｈ，“Linguistic

processingrelatedtospeechunderstandinginSPICOS

1ＥＩＣＥＴＲＡＮＳ・ＩＮＦ．＆ＳＹＳＴ.，ＶＯＬＥ７６－Ｄ，Ｎｏ．ｌＪＡＮＵＡＲＹｌ９９３
9４

YujiMoriyawasborninAichi，onFebruary22，1969．ＨｅｒｅｃｅｉｖｅｄｔｈｅＢＥ、
degreefomToyohashiUniversityof
Technology,Aichi，ｉｎｌ９９ＬＳｉｎｃｅｌ９９１，

11,”ｉｎＳｐｅｅｃｈＣｏｍｍ.,９，ｐｐ､565-585,1990.
(4)Kai，AandNakagawa，Ｓ，“ConsiderationonHMMbasedcontinuousspeechrecognitionusingwordspotting
methods,”ノEICETec/i"jMRepo〃（SPeec/i)，SP92-lO，hehasbeenastudentofMastercourseof

１９９２．

(5)Yamamoto，Ｍ､，Kobayashi，Ｓ・andNakagawa，Ｓ，“Ａｎａｎａｌｙｓｉｓａｎｄｐａｒｓｉｎｇｍｅｔｈｏｄｆｂｒｔheomissionofpost‐
positionandinversiononJapanesespokensentencesin

dialog,”ｉｎＰｒｏｃｑ/Ｙ/ｉｅ叩叩osi皿、。〃'Te１Ｍ〃/ｊＭｊｂ'2ｓ

Ｗ

Ｐ．～Ｆ，
dh.､

ｑ/､〃αZ皿'αノル"gⅢageprocessI"9,ｐｐ86-93,1992.
(6)Yamamoto，Ｍ,，Kobayashi，Ｓ・andNakagawa，Ｓ，“Aquerygenerationsystemtodisambiguatemeaningor

sentencesindialog,”Proceedi"ｇｓｑ／Ｚｈｅ肋肋"“αノColWe"ｃｅｑ／ＪＭＬ１３－８,ｐｐ５５１-554,199Ｌ

SeiichiNakagawawasbornin(7)Yamamoto，Ｈ，Kai，Ｋ，Osato，Ｍ・andShiino，Ｔ，

Kyoto,Japan,ｉｎ１９４８．ＨｅｒｅｃｅｉｖｅｄｔｈｅＢ
ＥａｎｄＭＥｄｅｇｒｅｅｓｉｎｅlectricalengineeringftomKyotolnstituteofTechnology，
Kyoto，ｉｎｌ９７１ａｎｄｌ９７３，respectively，
andDr,Eng・degreeltomKyotoUniver‐sity，Kyoto，ｉｎ1977．Hejoinedthe
FacultyofKyotoUniversity,ｉｎ1976,ａｓａ“DesignofanintelligentCAIsystemｆｂｒｔｒａｉｎｉｎｇａｌｂｒ－
ｅｉｇｎｌａｎｇｕａｇｅｂａsedonsimulationofconversation,，，刀α"s・〃＆ﾉＷｏＬ３０,ｎｏ､７，ｐｐ､908-917,1989.
(8)Yoshida,Ｈ,Yamamoto,Ｔ,Nomura,Ｙ,Yamashita,YandMizoguchi，Ｒ､，“UtilizationofthedialogHowindialogmanagementsystemMASCOTS,，,ProceedmgSq／〔ハピゴ//Ｍ"'、αノColZ/bだ"ｃｅｑ／ＪＭＬ１３－２,pp527－５３０，

ResearchAssociateintheDepartmentof

１９９１．

（９）Hasida,KandTakezawa,Ｔ､,“Aspectsofintegrationin
naturallanguageprocessing,”ＣＯ叩ｍｅｒＳｑ/ｉｌｗα'ｅｖｏＬ８，ｎｏ､６，ｐｐ３－１６，１９９１．

(１０）Nakagawa､Ｓ,“SpeechRecognitionBasedonStochastic
Model,，，IEICEl988．

(11）Ukita，Ｔ､，Ishikawa，MNakagawa，SandSakai，Ｔ，“Confirmationmethodsofutteranceｓｉｎａｄｉａｌｏｇｓｙｓｔｅｍ

ｂｙｓｐeech,”刀α"３.ＩＰＳＺｖｏｌ､22,ｎｏ､６，ｐｐ､589-595,1981．
(12）Wahlster，Ｗ・andKobsa，Ａ，“Usermodelsindialog
systems,”USC′ＭＯ火ZFi〃、ｉα/ｂｇＳ〕ﾉSZemS,AKobsaand
WWahlster（ｅｄ)，Springer-Verlag,ｐｐ４-34,1989.

InfbrmationSciences，Ｆｒｏｍｌ９８０ｔｏｌ９８３

ｈｅｗａｓａｎＡｓｓｉｓｔａｎｔProfbssor,ｆｍｍｌ９８３
tol990hewasanAssociateProfessoｒａｎｄｓｉｎｃｅｌ９９０ｈｅｈａｓｂｅｅｎ

ａＰｒｏｍｅｓｓorintheDepartmentofInfbrmationandComputer
Sciences，ToyohashiUniversityofTechnology，ＴｏｙｏｈａｓｈＬ
Ｄｕｒｉｎｇｌ９８５ｔｏｌ９８６，hewasavisitingscientistintheDepartmentofComputerScience，Carnegie-MellonUniversity，Ｐitts‐
burgh,ＵＳＡ・ＨｅｉｓａｎＡｕｔｈｏｒｏｆｔｈｅｂｏｏｋ``SpeechRecognition
BasedonStochasticModel，，（ｉｎJapanese)．Inst､Elect､Infbrln
Comm・Engrs・Japan，１９８８Ｄｒ,Nakagawawasacoreceipientof
theｌ９７７ＰａｐｅｒＡｗａｒｄｈｏｍｔｈｅＩＥＩＣＥａｎｄｔｈｅｌ９８８ＪＣＢｏｓｅ
ＭｅｍｏｒｉａｌＡｗａｒｄｆＴｏｍthelnstitutionofElectro・Telecomm．

Engrs．

ＭｉｋｉｏＹａｍａｍｏｔｏｗａｓｂｏｒｎｉｎ

Ｋｕｍａｍｏｔｏ,Japan，ｉｎｌ９６ＬＨｅｒｅｃｅｉｖｅｄ
ｔｈｅＢＥａｎｄＭ・Edegreesininformation
andcomputersciencesfromToyohashi
UniversityofTechnology，inl984and
l986HejoinedtheOKITechnoSystems
Laboratory，Inc，Nagoya，Ｊａｐａｎｆｉ｢ｏｍｌ９８６ｔｏｌ９８８ＨｅｉｓａｔｅｃｈｎｉｃａｌｏｆＴｉcialin

ToyohashiUniversityofTechnology．

Ｍr､Ｙａｍｏｍｏｔｏｉｓａｍｅｍｂｅｒｏｆｔｈｅｌｎｆｂｒ‐

mationProcessingSocietyofJapan，JapaneseSocietyofArtificialIntelligenceandAmericanAssocia‐
tionfbrArtificiallntelligence．

!くれい､;ﾛｰｰ,.~-:我．き憂塗;ﾐパ

ＳａｔｏshiKobayashiwasbornin
Yamanashi，Japan，ｉｎｌ９６６Ｈｅｒｅｃｅｉｖｅｄ
ｔｈｅＢＥｄｅｇｒｅｅｉｎｉnfbrmationandcomputersciencesfromToyohashiUniversityofTechnology，Aichi，ｉｎｌ９９ＬＳｉｎｃｅ
ｌ９９２，hehasbeenastudentofMasterfズ

courseoftheInfbrmationandComputer
Sciences，ToyohashiUniversityofTech‐
ｎｏlogyHeisengagedinresearchin
NaturalLanguageProcessing．

InfbrmationandComputerSciences，
ToyohashiUniversityofTechnology・Ｈｅｈａｓｂｅｅｎｅｎｇａｇｅｄｉｎｒｅｓｅａｒｃｈｏｎｔｈe
speechrecognitionandunderstanding
Systems、Ｍｒ・Moriyaisamemberof
InfbrmationProcessingSocietyofJapan．