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Recursive Neural Networks Dr. Kira Radinsky CTO SalesPredict Visi8ng Professor/Scien8st Technion Slides were adapted from lectures by Richard Socher

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Page 1: Recursive Neural Networks - Technion – Israel this assignment” Recursive Neural Networks for Structure Predicon Recursive Neural Networks for Structure Predicon A standard Recursive

RecursiveNeuralNetworks

Dr.KiraRadinskyCTOSalesPredictVisi8ngProfessor/Scien8stTechnion

SlideswereadaptedfromlecturesbyRichardSocher

Page 2: Recursive Neural Networks - Technion – Israel this assignment” Recursive Neural Networks for Structure Predicon Recursive Neural Networks for Structure Predicon A standard Recursive

Overview

Firsthour:RecursiveNeuralNetworks

•  Mo8va8on:Composi8onality

•  Structurepredic8on:Parsing

•  Backpropaga8onthroughStructure

•  VisionExample

Secondhour:

•  Matrix-VectorRNNs: Rela8onclassifica8on

•  RecursiveNeuralTensorNetworks: Sen8mentAnalysis

•  TreeLSTMs: PhraseSimilarity

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BuildingonWordVectorSpaceModels

x10 1 2 3 4 5 6 7 8 9 10

x25

4

3

2

1

92Monday

Tuesday 9.51.5

Buthowcanwerepresentthemeaningoflongerphrases?Bymappingthemintothesamevectorspace!

France 22.5

Germany 13

thecountryofmybirth theplacewhereIwasborn

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BuildingonWordVectorSpaceModels

x10 1 2 3 4 5 6 7 8 9 10

x25

4

3

2

1

92Monday

Tuesday 9.51.5

15

1.14

France 22.5

Germany 13

thecountryofmybirth theplacewhereIwasborn

Buthowcanwerepresentthemeaningoflongerphrases?Bymappingthemintothesamevectorspace!

Page 5: Recursive Neural Networks - Technion – Israel this assignment” Recursive Neural Networks for Structure Predicon Recursive Neural Networks for Structure Predicon A standard Recursive

Seman<cVectorSpaces

SingleWordVectors

•  Distribu8onalTechniques•  BrownClusters•  Usefulasfeaturesinside

models,e.g.CRFsforNER,etc.•  Cannotcapturelongerphrases

DocumentsVectors

•  Bagofwordsmodels•  PCA(LSA,LDA)•  GreatforIR,document

explora8on,etc.•  Ignorewordorder,no

detailedunderstanding

Vectorsrepresen8ngPhrasesandSentencesthatdonotignorewordorderandcaptureseman8csforNLPtasks

Page 6: Recursive Neural Networks - Technion – Israel this assignment” Recursive Neural Networks for Structure Predicon Recursive Neural Networks for Structure Predicon A standard Recursive

Howshouldwemapphrasesintoavectorspace?

0.40.3

the

2.33.6

birth

44.5

my

77

of

2.13.3

country

2.53.8

5.56.1

13.5

15

Useprincipleofcomposi8onalityThemeaning(vector)ofasentenceisdeterminedby(1) themeaningsofitswordsand(2) therulesthatcombinethem.

Modelsinthissec8oncanjointlylearnparsetreesandcomposi8onalvectorrepresenta8ons

x2

x10 1 2 3 4 5 6 7 8 9 10

5

4

3

2

1

thecountryofmybirth

theplacewhereIwasborn

Monday

Tuesday

GermanyFrance

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SentenceParsing:Whatwewant

NP

91

53

cat

71

sat

85

on

91

the

43

mat.The

NP

PP

S

VP

Page 8: Recursive Neural Networks - Technion – Israel this assignment” Recursive Neural Networks for Structure Predicon Recursive Neural Networks for Structure Predicon A standard Recursive

LearnStructureandRepresenta<on

NPNP

PP

S

VP

52 3

3

83

54

73

91

53

cat

71

sat

85

on

91

the

43

mat.The

Page 9: Recursive Neural Networks - Technion – Israel this assignment” Recursive Neural Networks for Structure Predicon Recursive Neural Networks for Structure Predicon A standard Recursive

WhyLearnStructureandRepresenta<on?

•  Thesyntac8crulesoflanguagearehighlyrecursive–needabeeermodeltorespectthat!

•  Wecannowinputsentencesofarbitrarylength•  wasahugeheadscratcherforusingNeuralNetsinNLP(see

tricksintroducedBengioetal.,2003;Henderson,2003;Collobert&Weston,2008)

•  Whynotuseword2vecinfraandlearnbigram,trigram,etc?•  infiniteamountofpossiblecombina8ons

ofwords.Storingandtraininganinfiniteamountofvectorswouldjustbeabsurd.

•  Somecombina8onsofwordswhiletheymightbecompletelyreasonabletohearinlanguage,mayneverberepresentedinourtraining/devcorpus.Sowewouldneverlearnthem.

Page 10: Recursive Neural Networks - Technion – Israel this assignment” Recursive Neural Networks for Structure Predicon Recursive Neural Networks for Structure Predicon A standard Recursive

Sidenote:Recursivevsrecurrentneuralnetworks

0.40.3

the

2.33.6

birth

44.5

my

77

of

2.13.3

country

2.53.8

5.56.1

13.5

15

0.40.3

the

2.33.6

birth

44.5

my

77

of

2.13.3

country

1 1 5.5 4.5 2.53.5 5 6.1 3.8 3.8

Page 11: Recursive Neural Networks - Technion – Israel this assignment” Recursive Neural Networks for Structure Predicon Recursive Neural Networks for Structure Predicon A standard Recursive

Sidenote:Arelanguagesrecursive?

•  Cogni8velydebatable•  But:recursionhelpfulindescribingnaturallanguage•  Example:“thechurchwhichhasnicewindows”,anounphrase

containingarela8veclausethatcontainsanounphrases•  Argumentsfornow:1)Helpfulindisambigua8on:

Page 12: Recursive Neural Networks - Technion – Israel this assignment” Recursive Neural Networks for Structure Predicon Recursive Neural Networks for Structure Predicon A standard Recursive

Sidenote:Arelanguagesrecursive?

2) Helpfulforsometaskstorefertospecificphrases:•  JohnandJanewenttoabigfes8val.Theyenjoyedthetripandthemusicthere.

•  “they”:JohnandJane(co-referenceresul8on)•  “thetrip”:wenttoabigfes8val•  “there”:bigfes8val

3) Labelinglessclearifspecifictoonlysubphrases•  Ilikedthebrightscreenbutnotthebuggyslowkeyboardofthephone.Itwasapaintotypewith.Itwasnicetolookat.

4) Worksbeeerforsometaskstousegramma8caltreestructure(butmaybewecanjusthaveaverydeepLSTMmodel?)

•  Thisiss8llupfordebate.

Page 13: Recursive Neural Networks - Technion – Israel this assignment” Recursive Neural Networks for Structure Predicon Recursive Neural Networks for Structure Predicon A standard Recursive

RecursiveNeuralNetworksforStructurePredic<on

mat.

91

the

43

33

83

85

33

Neural Network

83

1.3

Inputs:twocandidatechildren’srepresenta8onsOutputs:1.  Theseman8crepresenta8onifthetwonodesaremerged.2.  Scoreofhowplausiblethenewnodewouldbe.

85

on

Page 14: Recursive Neural Networks - Technion – Israel this assignment” Recursive Neural Networks for Structure Predicon Recursive Neural Networks for Structure Predicon A standard Recursive

RecursiveNeuralNetworkDefini<on

score=UTp

SameWparametersatallnodesofthetree

85

33

Neural Network

83

1.3score = =parent

c1 c2

p=tanh(Wc1+b),c2

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hisapointinthesamewordvectorspaceforthebigram”thisassignment”

RecursiveNeuralNetworksforStructurePredic<on

Page 16: Recursive Neural Networks - Technion – Israel this assignment” Recursive Neural Networks for Structure Predicon Recursive Neural Networks for Structure Predicon A standard Recursive

RecursiveNeuralNetworksforStructurePredic<on

AstandardRecursiveNeuralNetwork

Page 17: Recursive Neural Networks - Technion – Israel this assignment” Recursive Neural Networks for Structure Predicon Recursive Neural Networks for Structure Predicon A standard Recursive

ParsingasentencewithanRNN

Neural Network

0.120

Neural Network

0.410

Neural Network

2.333

91

5 7 8 9 43 1 5 1 3

Neural Network

3.152

Neural Network

0.301

The cat sat on the mat.

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Parsingasentence

91

52

Neural Network

1.121

Neural Network

0.120

Neural Network

0.410

Neural Network

2.333

5 7 8 9 43 1 5 1 3

18

The cat sat on the mat.

Page 19: Recursive Neural Networks - Technion – Israel this assignment” Recursive Neural Networks for Structure Predicon Recursive Neural Networks for Structure Predicon A standard Recursive

Parsingasentence

52

Neural Network

1.121

Neural Network

0.120

33

Neural Network

3.683

91

5 7 8 9 43 1 5 1 3

The

19

cat sat on the mat.

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Parsingasentence

54

73

83

52

33

91

53

85

91

43

71

The cat sat on the mat.

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Max-MarginFramework-Details

•  Thescoreofatreeiscomputedbythesumoftheparsingdecisionscoresateachnode:

85

33

RNN

831.3

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Max-MarginFramework---Details

•  Similartomax-marginparsing(Taskaretal.2004),asupervisedmax-marginobjec8ve.Maximizetheobjec8veof:

•  Theloss penalizesallincorrectdecisions

•  StructuresearchforA(x)wasmaximallygreedy•  Instead:BeamSearchwithChart

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Backpropaga<onThroughStructure

IntroducedbyGoller&Küchler(1996)

Principallythesameasgeneralbackpropaga8on

Threedifferencesresul8ngfromtherecursionandtreestructure:1.  Sumderiva8vesofWfromallnodes2.  Splitderiva8vesateachnode3.  Adderrormessages

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BTS:1)Sumderiva<vesofallnodesYoucanactuallyassumeit’sadifferentWateachnodeIntui8onviaexample:

Ifwetakeseparatederiva8vesofeachoccurrence,wegetsame:

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BTS:2)Splitderiva<vesateachnode

85

33

Duringforwardprop,theparentiscomputedusing2children83

c1

c1p= tanh(W c +b)2c2

85

33

Hence,theerrorsneedtobecomputedwrteachofthem:83

whereeachchild’serrorisn-dimensional

c1 c2

25

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BTS:3)Adderrormessages

•  Ateachnode:• Whatcameup(fprop)mustcomedown(bprop)•  Totalerrormessageserrormessagesfromparent+errormessagefromownscore

85

33

83

c1 c2

parentscore

Page 27: Recursive Neural Networks - Technion – Israel this assignment” Recursive Neural Networks for Structure Predicon Recursive Neural Networks for Structure Predicon A standard Recursive

BTSPythonCode:forwardProp

Many8mesyoucangetanoverflow(especiallywithRelu)– sothisisatricktosolvethis

Page 28: Recursive Neural Networks - Technion – Israel this assignment” Recursive Neural Networks for Structure Predicon Recursive Neural Networks for Structure Predicon A standard Recursive

BTSPythonCode:backProp

Upperleveldelta(deltat+1)

Errorsforscoresfromcurrentandprevious

Page 29: Recursive Neural Networks - Technion – Israel this assignment” Recursive Neural Networks for Structure Predicon Recursive Neural Networks for Structure Predicon A standard Recursive

BTS:Op<miza<on •  Asbefore,wecanplugthegradientsintoastandardoff-the-shelfL-BFGSop8mizerorSGD

•  BestresultswithAdaGrad(Duchietal,2011):

•  Fornon-con8nuousobjec8veusesubgradientmethod(Ratliffetal.2007)

26

Page 30: Recursive Neural Networks - Technion – Israel this assignment” Recursive Neural Networks for Structure Predicon Recursive Neural Networks for Structure Predicon A standard Recursive

Discussion:SimpleRNN•  GoodresultswithsinglematrixRNN(morelater)

•  SingleweightmatrixRNNcouldcapturesomephenomenabutnotadequateformorecomplex,higherordercomposi8onandparsinglongsentences

•  Thecomposi8onfunc8onisthesameforallsyntac8ccategories,punctua8on,etc

c2

Wscore

W

c1

sp

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Solu<on:Syntac<cally-Un<edRNN

•  Idea:Condi8onthecomposi8onfunc8ononthesyntac8ccategories,“un8etheweights”

•  Allowsfordifferentcomposi8onfunc8onsforpairsofsyntac8ccategories,e.g.Adv+AdjP,VP+NP

•  Combinesdiscretesyntac8ccategorieswithcon8nuousseman8cinforma8on

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Solu<on:Composi<onalVectorGrammars

•  Problem:Speed.Everycandidatescoreinbeamsearchneedsamatrix---vectorproduct.

•  Solu8on:Computescoreonlyforasubsetoftreescomingfromasimpler,fastermodel(PCFG)• Prunesveryunlikelycandidatesforspeed• Providescoarsesyntac8ccategoriesofthechildrenforeachbeamcandidate

•  Composi8onalVectorGrammars:CVG=PCFG+RNN

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Details:Composi<onalVectorGrammar

•  Scoresateachnodecomputedbycombina8onofPCFGandSU---RNN:

•  Interpreta8on:Factoringdiscreteandcon8nuousparsinginonemodel:

•  Socheretal.(2013)

Page 34: Recursive Neural Networks - Technion – Israel this assignment” Recursive Neural Networks for Structure Predicon Recursive Neural Networks for Structure Predicon A standard Recursive

Relatedworkforrecursiveneuralnetworks

Pollack(1990):Recursiveauto-associa8vememories PreviousRecursiveNeuralNetworksworkbyGoller&Küchler(1996),Costaetal.(2003)assumedfixedtreestructureandusedonehotvectors. Hinton(1990)andBoeou(2011):Relatedideasaboutrecursivemodelsandrecursiveoperatorsassmoothversionsoflogicopera8ons

Page 35: Recursive Neural Networks - Technion – Israel this assignment” Recursive Neural Networks for Structure Predicon Recursive Neural Networks for Structure Predicon A standard Recursive

RelatedWorkforparsing

•  Resul8ngCVGParserisrelatedtopreviousworkthatextendsPCFGparsers

•  KleinandManning(2003a):manualfeatureengineering•  Petrovetal.(2006):learningalgorithmthatsplitsandmerges

syntac8ccategories•  Lexicalizedparsers(Collins,2003;Charniak,2000):describeeach

categorywithalexicalitem•  HallandKlein(2012)combineseveralsuchannota8onschemesina

factoredparser.•  CVGsextendtheseideasfromdiscreterepresenta8onstoricher

con8nuousones

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Experiments•  StandardWSJsplit,labeledF1•  BasedonsimplePCFGwithfewerstates•  Fastpruningofsearchspace,fewmatrix---vectorproducts•  3.8%higherF1,20%fasterthanStanfordfactoredparser

Parser Test,AllSentences

StanfordPCFG,(KleinandManning,2003a) 85.5

StanfordFactored(KleinandManning,2003b) 86.6

FactoredPCFGs(HallandKlein,2012) 89.4

Collins(Collins,1997) 87.7

SSN(Henderson,2004) 89.4

BerkeleyParser(PetrovandKlein,2007) 90.1

CVG(RNN)(Socheretal.,ACL2013) 85.0

CVG(SU---RNN)(Socheretal.,ACL2013) 90.4

Charniak---SelfTrained(McCloskyetal.2006) 91.0

Charniak---SelfTrained---ReRanked(McCloskyetal.2006) 92.1

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SU---RNNAnalysis

•  Learnsno8onofsouheadwords

DT---NP

VP---NP

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Analysisofresul<ngvectorrepresenta<ons

Allthefiguresareadjustedforseasonalvaria8ons1. Allthenumbersareadjustedforseasonalfluctua8ons2. Allthefiguresareadjustedtoremoveusualseasonalpaeerns

Knight-Ridderwouldn’tcommentontheoffer1. Harscodeclinedtosaywhatcountryplacedtheorder2. Coastalwouldn’tdisclosetheterms

Salesgrewalmost7%to$UNKm.from$UNKm.1. Salesrosemorethan7%to$94.9m.from$88.3m.2. Salessurged40%toUNKb.yenfromUNKb.

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SU-RNNAnalysis •  Cantransferseman8cinforma8onfromsinglerelatedexample

•  Trainsentences:• Heeatsspaghewwithafork.• Sheeatsspaghewwithpork.

•  Testsentences• Heeatsspaghewwithaspoon.• Heeatsspaghewwithmeat.

Page 40: Recursive Neural Networks - Technion – Israel this assignment” Recursive Neural Networks for Structure Predicon Recursive Neural Networks for Structure Predicon A standard Recursive

SU---RNNAnalysis

Page 41: Recursive Neural Networks - Technion – Israel this assignment” Recursive Neural Networks for Structure Predicon Recursive Neural Networks for Structure Predicon A standard Recursive

LabelinginRecursiveNeuralNetworks

Neural Network

83

• Wecanuseeachnode’srepresenta8onasfeaturesforaso4maxclassifier:

•  Trainingsimilartomodelinpart1withstandardcross-entropyerror+scores

Softmax Layer

NP

Page 42: Recursive Neural Networks - Technion – Israel this assignment” Recursive Neural Networks for Structure Predicon Recursive Neural Networks for Structure Predicon A standard Recursive

SceneParsing

•  Themeaningofasceneimageisalsoafunc8onofsmallerregions,

•  howtheycombineaspartstoformlargerobjects,

•  andhowtheobjectsinteract.

Similarprincipleofcomposi8onality.

Page 43: Recursive Neural Networks - Technion – Israel this assignment” Recursive Neural Networks for Structure Predicon Recursive Neural Networks for Structure Predicon A standard Recursive

AlgorithmforParsingImages

SameRecursiveNeuralNetworkasfornaturallanguageparsing!(Socheretal.ICML2011)

ParsingNaturalSceneImages

Grass

PeopleBuilding

Tree

Seman<cRepresenta<onsFeatures

Segments

Page 44: Recursive Neural Networks - Technion – Israel this assignment” Recursive Neural Networks for Structure Predicon Recursive Neural Networks for Structure Predicon A standard Recursive

Mul<---classsegmenta<on

Method Accuracy

PixelCRF(Gouldetal.,ICCV2009) 74.3

Classifieronsuperpixelfeatures 75.9

Region---basedenergy(Gouldetal.,ICCV2009) 76.4

Locallabelling(Tighe&Lazebnik,ECCV2010) 76.9

SuperpixelMRF(Tighe&Lazebnik,ECCV2010) 77.5

SimultaneousMRF(Tighe&Lazebnik,ECCV2010) 77.5

RecursiveNeuralNetwork 78.1

StanfordBackgroundDataset(Gouldetal.2009)


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