ttic 31190: natural language processingkgimpel/teaching/31190/lectures/11.pdf · natural language...
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![Page 1: TTIC 31190: Natural Language Processingkgimpel/teaching/31190/lectures/11.pdf · Natural Language Processing Kevin Gimpel Winter 2016 Lecture 11: Recurrent and Convolutional Neural](https://reader030.vdocuments.mx/reader030/viewer/2022041022/5ed3f6068d46b66d22632b15/html5/thumbnails/1.jpg)
TTIC31190:NaturalLanguageProcessing
KevinGimpelWinter2016
Lecture11:RecurrentandConvolutionalNeuralNetworksinNLP
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Announcements• Assignment3assignedyesterday,dueFeb.29
• projectproposaldueTuesday,Feb.16
• midtermonThursday,Feb.18
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Roadmap• classification• words• lexicalsemantics• languagemodeling• sequencelabeling• neuralnetworkmethodsinNLP• syntaxandsyntacticparsing• semanticcompositionality• semanticparsing• unsupervisedlearning• machinetranslationandotherapplications
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2-transformation(1-layer)network
• we’llcallthisa“2-transformation”neuralnetwork,ora“1-layer”neuralnetwork
• inputvectoris• scorevectoris• onehiddenvector(“hiddenlayer”)
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vectoroflabelscores
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1-layerneuralnetworkforsentimentclassification
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Usesoftmax functiontoconvertscoresintoprobabilities
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ikr smh heaskedfiryo lastnamesohecanadduonfb lololol
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intj pronoun prepadj prep verbotherverb det noun pronoun
pronoun propernoun
verbprep intj
NeuralNetworksforTwitterPart-of-SpeechTagging
adj =adjectiveprep=prepositionintj =interjection
• inAssignment3,you’llbuildaneuralnetworkclassifiertopredictaword’sPOStagbasedonitscontext
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ikr smh heaskedfiryo lastnamesohecan
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intj pronoun prepadj prep verbotherverbdet noun pronoun
NeuralNetworksforTwitterPart-of-SpeechTagging
• e.g.,predicttagofyo givencontext• whatshouldtheinputxbe?– ithastobeindependentofthelabel– ithastobeafixed-lengthvector
![Page 9: TTIC 31190: Natural Language Processingkgimpel/teaching/31190/lectures/11.pdf · Natural Language Processing Kevin Gimpel Winter 2016 Lecture 11: Recurrent and Convolutional Neural](https://reader030.vdocuments.mx/reader030/viewer/2022041022/5ed3f6068d46b66d22632b15/html5/thumbnails/9.jpg)
ikr smh heaskedfiryo lastnamesohecan
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intj pronoun prepadj prep verbotherverbdet noun pronoun
NeuralNetworksforTwitterPart-of-SpeechTagging
• e.g.,predicttagofyo givencontext• whatshouldtheinputxbe?
wordvectorforyo
![Page 10: TTIC 31190: Natural Language Processingkgimpel/teaching/31190/lectures/11.pdf · Natural Language Processing Kevin Gimpel Winter 2016 Lecture 11: Recurrent and Convolutional Neural](https://reader030.vdocuments.mx/reader030/viewer/2022041022/5ed3f6068d46b66d22632b15/html5/thumbnails/10.jpg)
ikr smh heaskedfiryo lastnamesohecan
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intj pronoun prepadj prep verbotherverbdet noun pronoun
NeuralNetworksforTwitterPart-of-SpeechTagging
• e.g.,predicttagofyo givencontext• whatshouldtheinputxbe?
wordvectorforyowordvectorforfir
![Page 11: TTIC 31190: Natural Language Processingkgimpel/teaching/31190/lectures/11.pdf · Natural Language Processing Kevin Gimpel Winter 2016 Lecture 11: Recurrent and Convolutional Neural](https://reader030.vdocuments.mx/reader030/viewer/2022041022/5ed3f6068d46b66d22632b15/html5/thumbnails/11.jpg)
ikr smh heaskedfiryo lastnamesohecan
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intj pronoun prepadj prep verbotherverbdet noun pronoun
NeuralNetworksforTwitterPart-of-SpeechTagging
wordvectorforyowordvectorforfir
• whenusingwordvectorsaspartofinput,wecanalsotreatthemasmoreparameterstobelearned!
• thisiscalled“updating”or“fine-tuning”thevectors(sincetheyareinitializedusingsomethinglikeword2vec)
![Page 12: TTIC 31190: Natural Language Processingkgimpel/teaching/31190/lectures/11.pdf · Natural Language Processing Kevin Gimpel Winter 2016 Lecture 11: Recurrent and Convolutional Neural](https://reader030.vdocuments.mx/reader030/viewer/2022041022/5ed3f6068d46b66d22632b15/html5/thumbnails/12.jpg)
ikr smh heaskedfiryo lastnamesohecan
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intj pronoun prepadj prep verbotherverbdet noun pronoun
NeuralNetworksforTwitterPart-of-SpeechTagging
vectorforlastvectorforyo
• let’susethecenterword+twowordstotheright:
vectorforname
• ifname istotherightofyo,thenyo isprobablyaformofyour• butourx aboveusesseparatedimensionsforeachposition!
– i.e.,nameistwowordstotheright– whatifnameisonewordtotheright?
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FeaturesandFilters• wecoulduseafeaturethatreturns1ifnameistotherightofthecenterword,butthatdoesnotusetheword’sembedding
• howdoweincludeafeaturelike“awordsimilartoname appearssomewheretotherightofthecenterword”?
• ratherthanalwaysspecifyrelativepositionandembedding,wewanttoaddfilters thatlookforwordslikenameanywhereinthewindow(orsentence!)
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Filters• fornow,thinkofafilterasavectorinthewordvectorspace
• thefiltermatchesaparticularregionofthespace• “match”=“hashighdotproductwith”
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Convolution• convolutionalneuralnetworksuseabunchofsuchfilters
• eachfilterismatchedagainst(dotproductcomputedwith)eachwordintheentirecontextwindoworsentence
• e.g.,asinglefilterisavectorofsamelengthaswordvectors
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Convolution
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vectorforlastvectorforyo vectorforname
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Convolution
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vectorforlastvectorforyo vectorforname
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Convolution
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vectorforlastvectorforyo vectorforname
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Convolution
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vectorforlastvectorforyo vectorforname
=“featuremap”,hasanentryforeachwordposition incontextwindow/sentence
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Pooling
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vectorforlastvectorforyo vectorforname
=“featuremap”,hasanentryforeachwordposition incontextwindow/sentence
howdoweconvertthisintoafixed-lengthvector?usepooling:
max-pooling:returnsmaximumvalueinaverage pooling:returnsaverageofvaluesin
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Pooling
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vectorforlastvectorforyo vectorforname
=“featuremap”,hasanentryforeachwordposition incontextwindow/sentence
howdoweconvertthisintoafixed-lengthvector?usepooling:
max-pooling:returnsmaximumvalueinaverage pooling:returnsaverageofvaluesin
then,thissinglefilterproducesasinglefeaturevalue(theoutputofsomekindofpooling).inpractice,weusemanyfiltersofmanydifferentlengths(e.g.,n-gramsratherthanwords).
![Page 22: TTIC 31190: Natural Language Processingkgimpel/teaching/31190/lectures/11.pdf · Natural Language Processing Kevin Gimpel Winter 2016 Lecture 11: Recurrent and Convolutional Neural](https://reader030.vdocuments.mx/reader030/viewer/2022041022/5ed3f6068d46b66d22632b15/html5/thumbnails/22.jpg)
ConvolutionalNeuralNetworks• convolutionalneuralnetworks(convnets orCNNs)usefiltersthatare“convolvedwith”(matchedagainstallpositionsof)theinput
• informally,thinkofconvolutionas“performthesameoperationeverywhereontheinputinsomesystematicorder”
• “convolutionallayer”=setoffiltersthatareconvolvedwiththeinputvector(whetherx orhiddenvector)
• couldbefollowedbymoreconvolutionallayers,orbyatypeofpooling
• oftenusedinNLPtoconvertasentenceintoafeaturevector
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RecurrentNeuralNetworksInputisasequence:
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nottoobad
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RecurrentNeuralNetworksInputisasequence:
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“hiddenvector”
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RecurrentNeuralNetworks
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“hiddenvector”
![Page 26: TTIC 31190: Natural Language Processingkgimpel/teaching/31190/lectures/11.pdf · Natural Language Processing Kevin Gimpel Winter 2016 Lecture 11: Recurrent and Convolutional Neural](https://reader030.vdocuments.mx/reader030/viewer/2022041022/5ed3f6068d46b66d22632b15/html5/thumbnails/26.jpg)
Disclaimer• thesediagramsareoftenusefulforhelpingusunderstandandcommunicateneuralnetworkarchitectures
• buttheyrarelyhaveanysortofformalsemantics(unlikegraphicalmodels)
• theyaremorelikecartoons
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LongShort-TermMemoryRNNs(gateless)
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“memorycell”
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LongShort-TermMemoryRNNs(gateless)
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LongShort-TermMemoryRNNs(gateless)
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LongShort-TermMemoryRNNs(gateless)
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Experiment:textclassification• StanfordSentimentTreebank• binaryclassification(positive/negative)
• 25-dimwordvectors• 50-dimcell/hiddenvectors• classificationlayeronfinal hiddenvector• AdaGrad,10epochs,mini-batchsize10• earlystoppingondev set
accuracy
80.6
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OutputGates
31
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OutputGates
32
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OutputGates
33
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OutputGates
34
thisispointwisemultiplication!isavector
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OutputGates
35
thisispointwisemultiplication!isavector
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OutputGates
36
diagonalmatrix
logisticsigmoid,sooutputrangesfrom
0to1
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OutputGates
37
acc.
gateless 80.6
outputgates 81.9
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OutputGates
38
acc.
gateless 80.6
outputgates 81.9
What’sbeinglearned?(demo)
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InputGates
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InputGates
40
again,thisispointwise
multiplication
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InputGates
41
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InputGates
42
diagonalmatrix
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OutputGates
43
InputGates
difference
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InputGates
44
acc.
gateless 80.6
outputgates 81.9
inputgates 84.4
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InputandOutputGates
45
acc.
gateless 80.6
outputgates 81.9
inputgates 84.4
input&outputgates 84.6
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ForgetGates
46
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ForgetGates
47
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ForgetGates
48
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ForgetGates
49
acc.
gateless 80.6
outputgates 81.9
inputgates 84.4
forgetgates 82.1
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AllGates
50
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AllGates
51
acc.
gateless 80.6
outputgates 81.9
inputgates 84.4
input&outputgates 84.6
forgetgates 82.1
input&forget gates 84.1
forget& outputgates 82.6
input,forget,outputgates 85.3
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Backward&BidirectionalLSTMs
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bidirectional:ifshallow,justuseforwardandbackwardLSTMsinparallel,concatenatefinaltwohiddenvectors,feedtosoftmax
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Backward&BidirectionalLSTMs
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bidirectional:ifshallow,justuseforwardandbackwardLSTMsinparallel,concatenatefinaltwohiddenvectors,feedtosoftmax
forward backward
gateless 80.6 80.3
outputgates 81.9 83.7
inputgates 84.4 82.9
forgetgates 82.1 83.4
input,forget,outputgates 85.3 85.9
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Backward&BidirectionalLSTMs
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bidirectional:ifshallow,justuseforwardandbackwardLSTMsinparallel,concatenatefinaltwohiddenvectors,feedtosoftmax
forward backward bidirectional
gateless 80.6 80.3 81.5
outputgates 81.9 83.7 82.6
inputgates 84.4 82.9 83.9
forgetgates 82.1 83.4 83.1
input,forget,outputgates 85.3 85.9 85.1
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LSTM
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DeepLSTM(2-layer)
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layer1
layer2
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DeepLSTM(2-layer)
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layer1
layer2
acc.
gatelessshallow(50) 80.6
deep(30,30) 80.8
input,forget,outputshallow(50) 85.3
deep(30,30) ~85
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DeepBidirectionalLSTMs
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concatenatehiddenvectorsofforward&backwardLSTMs,connecteachentrytoforwardandbackwardhiddenvectorsinnextlayer
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(logistic)sigmoid: