How Contexts Matter Understanding in...
Transcript of How Contexts Matter Understanding in...
HowContextsMatterUnderstandinginDialoguesYUN-NUNG (VIVIAN)CHEN
§ Word-LevelContextsinSentences§ LearningfromPriorKnowledge–
Knowledge-Guided StructuralAttentionNetworks(K-SAN)[Chenetal.,‘16]§ LearningfromObservations–
ModularizingUnsupervisedSenseEmbedding (MUSE)[Lee&Chen,‘17]
§ Sentence-LevelContextsinDialogues§ InvestigationofUnderstanding Impact–
ReinforcementLearningBasedNeuralDialogueSystem[Lietal.,‘17]
§ Conclusion
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§ Dialoguesystemsareintelligentagentsthatareabletohelpusersfinishtasksmoreefficientlyviaconversationalinteractions.
§ Dialoguesystemsarebeingincorporatedintovariousdevices(smart-phones,smartTVs,in-carnavigatingsystem,etc).
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JARVIS– IronMan’sPersonalAssistant Baymax – PersonalHealthcareCompanion
§ Word-levelcontext§ Priorknowledgesuchaslinguistic syntax
§ Collocatedwords
§ Sentence-levelcontext
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Smartphone companiesincludingapple,blackberry,andsony willbeinvited.
showmetheflights fromseattle tosanfrancisco
(browsingactionmoviereviews…)Findmeagoodonethisweekend
LondonHasFalleniscurrentlythenumber1actionmovieinAmerica
request_movie(genre=action,date=thisweekend)
Howmisunderstanding influences thedialoguesystemperformance
Contextsprovide informativecuesforbetterunderstanding
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Knowledge-GuidedStructuralAttentionNetwork(K-SAN)
Y.-N.Chen,D.Hakkani-Tur,G.Tur,A.Celikyilmaz, J.Gao,andL.Deng,“KnowledgeasaTeacher:Knowledge-GuidedStructuralAttentionNetworks,”preprintarXiv:1609.00777, 2016.
§ Syntax(DependencyTree) § Semantics(AMRGraph)
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show
me
the
flights
from
seattle
to
san
francisco
ROOT
1.
3.
4.
2.
1.showme2.showflightsthe3.showflightsfromseattle4.showflightstofranciscosan
Sentence sshowmetheflightsfromseattle tosanfrancisco
Knowledge-GuidedSubstructurexi
(s/show:ARG0(y/you):ARG1(f/flight
:source(c/city:name(d/name:op1Seattle))
:destination(c2/city:name(s2/name:op1San:op2Francisco)))
:ARG2(i /I):modeimperative)
Knowledge-GuidedSubstructurexi1.showyou2.showflightseattle3.showflightsanfrancisco4.showi
show
you
flightI
1.
2.
4.
citycity
Seattle
SanFrancisco3..
Y.-N.Chen,D.Hakkani-Tur,G.Tur,A.Celikyilmaz, J.Gao,andL.Deng,“KnowledgeasaTeacher:Knowledge-GuidedStructuralAttentionNetworks,”preprintarXiv:1609.00777, 2016.
knowledge-guidedstructure{xi}
KnowledgeEncoding
SentenceEncoding
InnerProduct
u
mi
KnowledgeAttention Distributionpi
EncodedKnowledgeRepresentation WeightedSum
∑
h
o
Knowledge-GuidedRepresentation
slottaggingsequence
s
y
show me the flights from seattle to san francisco
ROOT
InputSentence
ht-1 ht+1htW W W W
wt-1
yt-1
U
Mwt
U
wt+1
U
Vyt
Vyt+1
V
MM
RNNTagger
KnowledgeEncodingModule
CNNkg
CNNin NNout
7Y.-N.Chen,D.Hakkani-Tur,G.Tur,A.Celikyilmaz, J.Gao,andL.Deng,“KnowledgeasaTeacher:Knowledge-GuidedStructuralAttentionNetworks,”preprintarXiv:1609.00777, 2016.
Themodelwillpaymoreattentiontomoreimportantsubstructures thatmaybecrucialforslottagging.
§ Darkerblocksandlinescorrespondtohigherattentionweights
8Y.-N.Chen,D.Hakkani-Tur,G.Tur,A.Celikyilmaz, J.Gao,andL.Deng,“KnowledgeasaTeacher:Knowledge-GuidedStructuralAttentionNetworks,”preprintarXiv:1609.00777, 2016.
§ Darkerblocksandlinescorrespondtohigherattentionweights
K-SANlearnsthesimilarattentiontosalientsubstructureswithlesstrainingdata
9Y.-N.Chen,D.Hakkani-Tur,G.Tur,A.Celikyilmaz, J.Gao,andL.Deng,“KnowledgeasaTeacher:Knowledge-GuidedStructuralAttentionNetworks,”preprintarXiv:1609.00777, 2016.
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ModularizingUnsupervisedSenseEmbeddings (MUSE)
G.-H.LeeandY.-N.Chen, “MUSE:ModularizingUnsupervised SenseEmbeddings,” inEMNLP,2017.
§ Wordembeddings aretrainedonacorpusinanunsupervisedmanner
§ Usingthesameembeddings fordifferentsenses forNLPtasks,e.g.NLU,POStagging
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FinallyIchoseGoogle insteadofApple.
Canyoubuymeabagof apples,oranges,andbananas?
G.-H.LeeandY.-N.Chen, “MUSE:ModularizingUnsupervised SenseEmbeddings,” inEMNLP,2017.
Wordswithdifferent sensesshouldcorrespond differentembeddings
Smartphone companiesincluding blackberry,andsony willbeinvited.
§ Input:unannotatedtextcorpus
§ Twokeymechanisms§ Senseselection givenatextcontext§ Senserepresentation toembedstatisticalcharacteristicsofsenseidentity
G.-H.LeeandY.-N.Chen, “MUSE:ModularizingUnsupervised SenseEmbeddings,” inEMNLP,2017.
apple
apple-1 apple-2senseselection
senseembedding
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§ Senseselection§ Policy-based
§ Value-based
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Corpus:{Smartphonecompaniesincludingapple blackberry,andsonywillbeinvited.}
senseselection←
rewardsignal←
senseselection→
samplecollocation1
2
2
3
Senseselectionforcollocatedword𝐶$%
SenseSelectionModule
…𝐶$' = 𝑤*𝐶$'+,
𝑞(𝑧*,|𝐶$') 𝑞(𝑧*2|𝐶$') 𝑞(𝑧*3|𝐶$')
matrix𝑄*
matrix𝑃
… 𝐶$'6,apple andincluding sonyblackberry
𝑧7,
SenseRepresentationModule
…𝑃(𝑧*2|𝑧7,) 𝑃(𝑧89|𝑧7,)
negativesampling
matrix𝑉
matrix𝑈
§ Senserepresentationlearning
§ Skip-gramapproximation
SenseSelectionModule
…𝐶$ = 𝑤7𝐶$+,
𝑞(𝑧7,|𝐶$< ) 𝑞(𝑧72|𝐶$< ) 𝑞(𝑧73|𝐶$< )
Senseselectionfortargetword𝐶$
matrix𝑄7
matrix𝑃
… 𝐶$6,including apple blackberrycompanies and
Collocatedlikelihood servesasarewardsignaltooptimizethesenseselectionmodule.
§ Dataset:SCWSformulti-senseembeddingevaluation
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Approach MaxSimC AvgSimCHuangetal.,2012 26.1 65.7Neelakantan etal.,2014 60.1 69.3Tianetal.,2014 63.6 65.4Li&Jurafsky,2015 66.6 66.8Bartunov etal.,2016 53.8 61.2Qiu etal.,2016 64.9 66.1MUSE-Policy 66.1 67.4MUSE-Greedy 66.3 68.3MUSE-ε-Greedy 67.4+ 68.6
Heborrowedthemoney frombanks. Iliveneartoariver. correlation=?
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Context …bravesfinishtheseasonintiewiththelosangelesdodgers…
…hislateryearsproudlyworetiewiththechinese charactersfor…
k-NN scorelessotl shootout6-6hingis 3-37-70-0
pantstrousersshirtjuventusblazersocksanfield
Figure
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Context …ofthemulberryortheblackberry andminos senthimto…
…ofthelargenumberofblackberry usersintheusfederal…
k-NN cranberriesmaplevacciniumapricotapple
smartphonessapmicrosoft ipv6smartphone
Figure
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Context …shellsand/orhighexplosivesquashhead and/oranti-tank…
… headwasshaventoprevent head liceseriousthreatbackthen…
…appointjohnpoperepublicanashead ofthenewarmyof…
k-NN venterthoraxneckspearmillimetersfusiform
shavedthatcherlokithoraxmao luther chest
multi-partyappointsunicameralberiaappointed
Figure
MUSElearnssenseembeddings inanunsupervised wayandachievesthefirstpurelysense-level representation learningsystemwithlinear-timesenseselection
RL-BasedNeuralDialogueSystems
X.Li,Y.-N.Chen, L.Li,J.Gao,andA.Celikyilmaz, “End-to-EndTask-Completion NeuralDialogueSystems,”inIJCNLP,2017.
§ Dialoguemanagement isframedasareinforcementlearning task
§ Agentlearnstoselectactionstomaximizetheexpectedreward
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Environment
Observation
Action
Reward
Ifbookingarightticket,reward=+30
Iffailing,reward=-30
Otherwise,reward=-1
Agent
X.Li,Y.-N.Chen, L.Li,J.Gao,andA.Celikyilmaz, “End-to-EndTask-Completion NeuralDialogueSystems,”inIJCNLP,2017.
§ Dialoguemanagement isframedasareinforcementlearning task
§ Agentlearnstoselectactionstomaximizetheexpectedreward
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Environment
Observation
Action
Agent
NaturalLanguageGeneration
UserAgendaModeling
UserSimulator
LanguageUnderstanding
DialogueManagement
NeuralDialogueSystem
TextInput:Arethereanyactionmovies toseethisweekend?
DialoguePolicy:request_location
X.Li,Y.-N.Chen, L.Li,J.Gao,andA.Celikyilmaz, “End-to-EndTask-Completion NeuralDialogueSystems,”inIJCNLP,2017.
X.Li,Y.-N.Chen, L.Li,J.Gao,andA.Celikyilmaz, “End-to-EndTask-Completion NeuralDialogueSystems,”inIJCNLP,2017.
wi
<slot>
wi+1
O
EOS
<intent>
wi
<slot>
wi+1
O
EOS
<intent>
§ LU,DST(neuraldialoguesystem),andNLG(usersimulation)aretrainedinsupervisedway
§ End-to-endtrainingfordialoguepolicylearning
KnowledgeDatabase
NeuralDialogueSystem
UserModel
UserSimulation
DialoguePolicy
NaturalLanguage
w0
w1 w2
NLGEOS
UserGoal
wi
<slot>
wi+1
O
EOS
<intent>
LU
𝑠$DST
𝑠, 𝑠2 𝑠>
𝑎, 𝑎2 𝑎@
……
…
DialoguePolicyLearning
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Usergoal – Twoticketsfor“Deadpool” tomorrow9PMatAMCPacificPlace11theater,Seattle.
RULE BASEDSYSTEMUser: Doyouhaveanyticketformovie‘Deadpool’at9:00pm?Agent:Whichtheaterdoyouwant?User: AMCpacificplace11theater,please!Agent:Howmanyticketsdoyouwant?User: Two!Agent:Whatdate?User: Tomorrow!Agent:Whichcitydoyouwant?User: Seattle!Agent:Doyouhaveanyspecification forzipcode?User: No,Idonotcare.Agent:Doyouhaveanydistanceconstraint?User: No,Idonotcare.Agent:Howaboutvideoformat?2d?Imax3d?User: Anykindisokay.Agent:Doyouhaveanyconstrainttoprice?User: No!Agent:Okay,Iwill issue2ticketsforyou,tomorrow9:00pmatAMCpacificplace11theater,Seattle,movie‘Deadpool’.User: Okay,thanks.Agent:Thanks!
REINFORCEMENT LEARNINGSYSTEMUser: Iwanttwoticketsformovie‘Deadpool’.Agent:Whatisthestarttime youprefer?User: 9:00PMAgent:Whichday?User: TomorrowAgent:Whichcity?User: SeattleAgent:Whichtheaterdoyouprefer?User: AMCPacificPlace11theater.Agent:Okay,Iwill issue2ticketsforyou,tomorrow9:00pmatAMCpacificplace11theater,Seattle,movie‘Deadpool’.User: Okay,thanks.Agent:Thanks!
Skiptherequeststheusermaynotcareabout toimproveefficiency
Issue1:nonotionaboutwhatrequestscanbeskipped
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Usergoal – Twoticketsfor“Hail Caesar”tomorrow7:15PMatAMCLowesOaktree6,Seattle.
Automaticallylearnwhenandwhattoconfirmforbetterrobustness
REINFORCEMENT LEARNINGSYSTEMAgent:Whatstarttimedoyouprefer?User: 7:15pmTomorrowAgent:Whichcityyouarelookingfor?User: SeattleAgent:Okay,Iwill issueyouticketsformovie‘HailCaeser ’tomorrow7:50pmattheaterAMCLowesOakTree6,Seattle.User: Oh,no.Agent: Itis7:50pm,right?User: Twoticketsfor7:15pm,please.Agent:Okay,Iwill issueyoutwoticketsformovie‘HailCaeser ’tomorrow7:15pmattheaterAMCLowesOakTree6,Seattle.User: Thanks!Agent:Thanks!
RULE BASEDSYSTEMUser: Doyouanyticketformovie‘HailCaesar ’attheaterAMCLowesOakTree6?Agent:Whatstarttimedoyouprefer?User: 7:15pm TomorrowAgent:Whichcityyouarelookingfor?User: SeattleAgent:Howmanyticketsdoyouwant?User: TwoAgent:Okay,Ihaveissuedtwotickets formovie‘HailCaeser ’tomorrow7:50pmattheater AMCLowesOakTree6,Seattle.User: Oh,no.Agent:Thanks!
Issue2:noprinciplewaytodealwithLUerrors/uncertainty
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§ Userspresentedwithapredefinedusergoal
§ Userrating:1(worst)to5(best)basedonbothnaturalness andcoherence ofthedialogue
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§ Word-levelcontextsinsentenceshelpunderstandwordmeanings§ LearningfromPriorKnowledge–
K-SANachievesbetterLUviaknownknowledge [Chenetal.,‘16]§ LearningfromObservations–
MUSElearnssenseembeddings withefficientsenseselection[Lee&Chen,‘17]
§ Sentence-levelcontextshavedifferentimpactsondialogueperformance§ InvestigationofUnderstanding Impact–
Sloterrorsdegradesystemperformancemorethanintenterrors[Lietal.,‘17]
§ Contextsfromdifferentlevelsprovidecuesforbetterunderstandinginsupervisedandunsupervisedways
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QA