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NarrativeStoryGenerationASkeleton-BasedModelforPromotingCoherence 4-11-2018
JingjingXu,XuanchengRen,Yi Zhang,QiZeng, Xiaoyan Cai,XuSun
MOEKeyLabofComputationalLinguistics,SchoolofEECS,PekingUniversitySchoolofAutomation,NorthwesternPolytechnical University
jingjingxu@pku.edu.cn
ASkeleton-BasedModelforPromotingCoherenceAmongSentencesinNarrativeStoryGeneration
NarrativeStoryGenerationASkeleton-BasedModelforPromotingCoherence 4-11-2018 2 of28
p Introduction
Ø Task
Ø Challenge
p Skeleton-BasedModel
Ø Overview
Ø Skeleton-BasedGenerativeModule
Ø SkeletonExtractionModule
Outline
p Experiment
Ø Dataset
Ø Baselines
Ø Results
p Analysis
Ø IncrementalAnalysis
Ø ErrorAnalysis
p Conclusion
Introduction
NarrativeStoryGenerationASkeleton-BasedModelforPromotingCoherence 4-11-2018 4 of28
NarrativeStoryGeneration
TaskDefinitionInput:Ashort description ofasceneoranevent.Output:Arelevant narrativestoryfollowingtheinput.
Input:Fans cametogethertocelebrate theopening ofanewstudio foranartist.
Output:Theartist providedchampagne influtesforeveryone.Friendstoasted andcheered theartistassheopened hernewstudio.
Input: LastweekIattendedawedding forthefirsttime.
Output: Therewerealotoffamilies there.Theywerealltakingpictures together.Everyonewasveryhappy.Thebride andgroom gottorideinalimothattheyrented.
NarrativeStoryGenerationASkeleton-BasedModelforPromotingCoherence 4-11-2018 5 of28
q Highrequirementsontightsemanticconnectionamongsentences
q Ifthegiveninputisabout“artistandstudioopening”
Theartistprovidedchampagneforeveryone
Fanstoastedandcheeredtheartist
Thefanswereveryhappytoseethisevent
Thefootballgameissoexciting
Theweatherissocoldandalltreesarecoveredwithheavysnow
Difficulty
NarrativeStoryGenerationASkeleton-BasedModelforPromotingCoherence 4-11-2018 6 of28
Motivation
q Theconnectionamongsentencesismainlyreflectedthroughkeyphrases (orskeleton)
q Weexplicitlymodeltherelationamongskeletonsinthiswork.
NarrativeStoryGenerationASkeleton-BasedModelforPromotingCoherence 4-11-2018 7 of28
q Motivatedbythisfact:
1.Wedonotgenerateasentencefromlefttoright
2.Instead,wefirstgenerateaskeleton
3.Expandtheskeletontoacompleteandfluentsentence
Skeleton-basedModel
Approach
NarrativeStoryGenerationASkeleton-BasedModelforPromotingCoherence 4-11-2018 9 of28
q Ourmodelcontainstwoparts
Ø Skeleton-basedgenerativemodule
1. Input-to-skeletoncomponent
2. Skeleton-to-targetcomponent
Ø Skeletonextractionmodule
Overview
NarrativeStoryGenerationASkeleton-BasedModelforPromotingCoherence 4-11-2018 10 of28
q GenerationProcess
Skeleton-BasedGenerativeModule
Fanscametogethertocelebratetheopeningofanewstudioforanartist.
The artist provided champagne in flutes for everyone.
artist champagne
Friends toasted cheered artist.
Friends toasted and cheered the artist as she opened her new studio.
NarrativeStoryGenerationASkeleton-BasedModelforPromotingCoherence 4-11-2018 11 of28
q Input-to-SkeletonComponent
Ø Learnthedependencybetweensourceinputsandtargetskeletons
Ø Model:ASeq2Seq
Ø TrainingLoss:Cross-entropyloss
q Skeleton-to-TargetComponent
Ø Expandaskeletontoacompletesentence
Ø Model:Seq2Seq
Ø Encoder:LSTM
Ø Decoder:LSTM
Ø TrainingLoss:Cross-EntropyLoss
TheStructureofSkeleton-BasedGenerativeModule
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q SkeletonsassupervisorysignalØ Inreal-worlddatasets,thehuman-annotatedskeletonisusuallyunavailable
q Rule-basedapproaches
Ø Advantage:Simple
Ø Drawback:Difficulttodefinetheunifiedrulesofextractingskeletons
q Toaddressthisproblem,webuildaskeletonextractionmoduletoautomaticallyexploresentenceskeletons
Skeleton-ExtractiveModule
NarrativeStoryGenerationASkeleton-BasedModelforPromotingCoherence 4-11-2018 13 of28
q Reformulateskeletonextractionasasentencecompressionproblem
Ø Advantage:sentencecompressionrequiressystemstokeepcore
information,whichisconsistentwithourtarget.
Ø Drawback:sentencecompressionresultsusuallyaregrammatical,which
limitsthequalityofskeletons.
Skeleton-ExtractionModule
Thelady wearingthepinkshirtdecidedtostopplayingthevideoandchattedwithotherguests.
Lady decidedtostopplayingthevideo
compress
NarrativeStoryGenerationASkeleton-BasedModelforPromotingCoherence 4-11-2018 14 of28
q Sequencetosequencemodel
Ø Encoder:LSTM
Ø Decoder:LSTM
q Input:originaltext
q Output:theskeletonoftheoriginaltext
q Lossfunction:cross-entropyloss
ThestructureofSkeletonExtractionModule
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qMotivation
q Usethefeedbackofgenerativemoduletoimproveit
q Challenge
q Lossisnolongerdifferentiable
q Approach
q ReinforcementLearningMethod
q Treatthefeedbackofgenerativemoduleasreward
q Optimizetheextractionmodelby
Howtoimprovethequalityofskeleton-extractionmodule
NarrativeStoryGenerationASkeleton-BasedModelforPromotingCoherence 4-11-2018 16 of28
qMultiplicationofthecross-entropyloss
• K:Upperboundofthereward,
• R1:Cross-entropylossesintheinput-to-skeletoncomponent
• R2:Cross-entropylossesintheskeleton-to-targetcomponent,respectively.
Reward
Experiment
NarrativeStoryGenerationASkeleton-BasedModelforPromotingCoherence 4-11-2018 18 of28
q Source:
ü VisualStorytelling
q Dataset:
ü Input:thefirstsentence
ü Output:thefollowingsentences
Dataset
Having a good time bonding and talking.
[M] got exhausted by the heat.
Sky illuminated with a brilliance of gold and orange hues.
NarrativeStoryGenerationASkeleton-BasedModelforPromotingCoherence 4-11-2018 19 of28
q Entity-EnhancedSeq2SeqModel(EESeq2Seq)
ü Combineentitycontextandtextcontexttogether
q Dependency-TreeEnhancedSeq2SeqModel(DE-Seq2Seq)
ü Basedondependencyparsinglabels
q Generalized-TemplateEnhancedSeq2SeqModel(GE-Seq2Seq)
ü Useexistingknowledgebases(WordNet,VerbNet)togetageneralized
sentencerepresentation
Baselines
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q AutomaticEvaluation
Results
NarrativeStoryGenerationASkeleton-BasedModelforPromotingCoherence 4-11-2018 21 of28
q HumanEvaluation
ü 100items
ü Fluencyandcoherence
Results
NarrativeStoryGenerationASkeleton-BasedModelforPromotingCoherence 4-11-2018 22 of28
q Input1:[Female]andherfriendhadanightoutonthetown.
Ø EE-Seq2Seq:Theywereveryhappytoseeus.[Male]and[female]were
gettingmarried today.
Ø DE-Seq2Seq:Wewalkedthroughthestreetsofthecityandfoundaplaceto
sit.Thewholefamilycameouttosupporttheirfamilies.
Ø GE-Seq2Seq:MyfriendsandIwenttoabarlastnight.[Female]wasso
happytobethere.
Ø ProposedModel:Theywenttothebar.Theyhadagreattime.
Generatedexamples
Analysis
NarrativeStoryGenerationASkeleton-BasedModelforPromotingCoherence 4-11-2018 24 of28
q 4typesoferrors
ü Irrelevantscene
ü Chaoticsyntax
ü Chaotictimeline
ü Repeatedscene
ErrorAnalysis
NarrativeStoryGenerationASkeleton-BasedModelforPromotingCoherence 4-11-2018 25 of28
A. Weproposeanewskeleton-basedmodelforgeneratingcoherentnarrativestories
B. Experimentalresultsshowthatourmodelsignificantlyimprovesthequalityofgeneratedstories
C. Erroranalysisshowsthattherearestillmanychallengesinnarrativestorygeneration,whichwe
wouldliketoexploreinthefuture
Conclusion
NarrativeStoryGenerationASkeleton-BasedModelforPromotingCoherence 4-11-2018 26 of28
Thank You!
Ifyouhaveanyquestion,pleasesendane-mailtojingjingxu@pku.edu.cn
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