Sequence to Sequence Learning · joint work with many Google Brain collaborators Sequence to...
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Quoc V. Le joint work with many Google Brain collaborators Sequence to Sequence Learning
Transcript of Sequence to Sequence Learning · joint work with many Google Brain collaborators Sequence to...
Quoc V. Le
joint work with many Google Brain collaborators
Sequence to Sequence Learning
brainvision summit
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Q1-2012 Q3-2012 Q1-2013 Q3-2013 Q1-2014 Q3-2014 Q1-2015 Q3-2015
Projects use GoogleBrain
Time
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Q1-2012 Q3-2012 Q1-2013 Q3-2013 Q1-2014 Q3-2014 Q1-2015 Q3-2015
Significant speech & vision improvements
Sequence to sequence models
TensorFlow launched
SmartReply
RankBrain
Directories use
GoogleBrain
Time
Discovery of Cat Detector on YouTube
Google Photos
Word2Vec
0
300
600
900
1200
Q1-2012 Q3-2012 Q1-2013 Q3-2013 Q1-2014 Q3-2014 Q1-2015 Q3-2015
Significant speech & vision improvements
TensorFlow launched
SmartReply
RankBrain
Directories use
GoogleBrain
Time
Discovery of Cat Detector on YouTube
Google Photos
Word2Vec
Sequence to sequence models
Machines that understand natural language
Step 1: Understand words
Machines that understand natural language
Step 1: Understand words
Step 2: Understand strings of words
Machines that understand natural language
Step 1: Understand words
Step 2: Understand strings of words
a aa dogcat
. . .
zzz
100 dimensions . . .
the
. . .
sat
. . .. . .
aardvark
the cat sat on the mat
a aa dogcat
. . .
zzz
100 dimensions . . .
the
. . .
sat
. . .. . .
lovelikeadoreking
queenprincess
prince
saturdaysundaymonday
manwoman
googlefacebook microsoft
ibm
hellohi
goodbye
englandfrance
russiachina
smallsmaller
bigbigger
first principal component
lovelikeadoreking
queenprincess
prince
saturdaysundaymonday
manwoman
googlefacebook microsoft
ibm
hellohi
goodbye
englandfrance
russiachina
smallsmaller
bigbigger
first principal component
lovelikeadoreking
queenprincess
prince
saturdaysundaymonday
manwoman
googlefacebook microsoft
ibm
hellohi
goodbye
englandfrance
russiachina
smallsmaller
bigbigger
first principal component
four
five
three
two
one
English word vectors for numbers
four
five
three
two
one
cuatro
cinco
tres
dos
uno
Spanish word vectors for numbers
English word vectors for numbers
horsecow dog
cat
pig
caballovaca
cerdo
gato
perro
Spanish word vectors for animals
English word vectors for animals
horsecow dog
cat
pig
caballovaca
cerdo
gato
perro
Spanish word vectors for animals
English word vectors for animals
Mikolov, Le, Sutskever Exploiting Similarities among Languages for
Machine Translation. arXiv, 2013
Machines that understand natural language
Step 1: Understand words
Step 2: Understand strings of words
i love music
i like mathematics
i enjoy computer science
me encanta la musica
Spanish “sentence” vectorsEnglish “sentence” vectors
me gustan las matemáticas
me gusta disfrutar de la informática
i
me encanta la musica
<start> me encanta lalove music musica
<stop>
i
me encanta la musica
<start> me encanta lalove music musica
<stop>
“end-to-end training”Sutskever, Vinyals, Le. Sequence to Sequence Learning
with Neural Networks. NIPS, 2014. Related Approach: Cho & Bengio. Encoder-Decoder RNNs.
i
brainvision summit
lovemusic
<start>
meencantala
musica<stop>
i
me encanta la musica
<start> me encanta lalove music musica
<stop>
Mary admires John
Mary is in love with John
Mary respects John
Mary admires John
Mary is in love with John
Mary respects John
John admires Mary
John is in love with Mary
John respects Mary
Quoc: Hello, I have a problem with my machine Machine: Hi, this is Shiva. How may I assist you today? Quoc: I want to access using VPN Machine: Currently, the machine is connected to corporate network? Quoc: No Machine: Check out this solution <URL> Quoc: Thanks , bye! Machine: Thank you.
Quoc: What is the purpose of life? Machine: To serve the greater good.
Quoc: What is the purpose of living? Machine: To live forever.
Quoc: What is the purpose of dying? Machine: To have a life.
Vinyals, Le. A Neural Conversational Model. ICML Deep Learning Workshop, 2015.
SmartReply
Machines that understand natural language
Step 1: Understand words
Step 2: Understand strings of words
Step 3: Have memory, logical reasoning
Advances in Memory and Logical Reasoning
“Soft” Attention Mechanism (Bahdanau, Cho, Bengio, 2014)
Augmented Memory (Graves et al, 2014; Weston et al, 2014)
Augmented Logic and Arithmetic Components (Neelakatan, Le, Sutskever, 2016)
i
me encanta la musica
<start> me encanta lalove music musica
<end>
i
me
<start>love music
i
me
<start>love music
i
me
<start>love music
i
me
<start>love music
Bahdanau, Cho, Bengio. Neural Machine Translation by Jointly Learning to Align and Translate. ICLR, 2015.
i
me
<start>love musicExternal Memory
Graves, Wayne, Danihelka. Neural Turing Machines. ArXiv, 2014 Sukhabaatar, Szlam, Weston, Fergus.
End-to-end memory networks. NIPS, 2015.
i
me
<start>love musicExternal Memory
Arithmetic& Logic
Functions
Neelakantan, Le, Sutskever. Neural Programmer: Inducing Latent Programs with Gradient Descent. ICLR, 2016.
add(a,b)subtract(a,b)multiply(a,b)
…
i
me
<start>love musicExternal Memory
Arithmetic& Logic
Functions
Neelakantan, Le, Sutskever. Neural Programmer: Inducing Latent Programs with Gradient Descent. ICLR, 2016.
add(a,b)subtract(a,b)multiply(a,b)
…
Quoc: I have 2 apples and Tom gives me 3 apples. How many apples do I have?
Machine: 5 apples.
Quoc: I have 2 apples and Tom gives me 3 apples. How many apples do I have?
brainvision summit
External Memory
Arithmetic & LogicFunctions
add(a,b)subtract(a,b)multiply(a,b)
…
Quoc: I have 2 apples and Tom gives me 3 apples. How many apples do I have?
brainvision summit
External Memory
Arithmetic & LogicFunctions
add(a,b)subtract(a,b)multiply(a,b)
…
Quoc: I have 2 apples and Tom gives me 3 apples. How many apples do I have?
brainvision summit
External Memory
Arithmetic & LogicFunctions
add(a,b)subtract(a,b)multiply(a,b)
…
Quoc: I have 2 apples and Tom gives me 3 apples. How many apples do I have?
brainvision summit
External Memory
Arithmetic & LogicFunctions
add(a,b)subtract(a,b)multiply(a,b)
…
Quoc: I have 2 apples and Tom gives me 3 apples. How many apples do I have?
brainvision summit
External Memory
Arithmetic & LogicFunctions
add(a,b)subtract(a,b)multiply(a,b)
…
2
Quoc: I have 2 apples and Tom gives me 3 apples. How many apples do I have?
brainvision summit
External Memory
Arithmetic & LogicFunctions
add(a,b)subtract(a,b)multiply(a,b)
…
2,3
Quoc: I have 2 apples and Tom gives me 3 apples. How many apples do I have?
brainvision summit
External Memory
Arithmetic & LogicFunctions
add(a,b)subtract(a,b)multiply(a,b)
…
2,3
Quoc: I have 2 apples and Tom gives me 3 apples. How many apples do I have?
brainvision summit
External Memory
Arithmetic & LogicFunctions
add(a,b) = 5subtract(a,b) = -1multiply(a,b) = 6
…
2,3
Quoc: I have 2 apples and Tom gives me 3 apples. How many apples do I have?
brainvision summit
External Memory
Arithmetic & LogicFunctions
add(a,b) = 5 * w1subtract(a,b) = -1 * w2multiply(a,b) = 6 * w3
…
2,3
Quoc: I have 2 apples and Tom gives me 3 apples. How many apples do I have?
brainvision summit
External Memory
Arithmetic & LogicFunctions
add(a,b) = 5 * w1subtract(a,b) = -1 * w2multiply(a,b) = 6 * w3
…
2,3
brainvision summit
External Memory
Arithmetic & LogicFunctions
add(a,b) = 5 * w1subtract(a,b) = -1 * w2multiply(a,b) = 6 * w3
…
2,3
Quoc: I have 2 apples and Tom gives me 3 apples. How many apples do I have?
Machine: 5 apples.
Machines that understand natural language
Step 1: Understand words
Step 2: Understand strings of words
Step 3: Have memory, logical reasoning
Step 4: Learn from many tasks
English (unsupervised)
German (translation)
Tags (parsing)English
English (unsupervised)
German (translation)
Tags (parsing)English
One-to-many
English (unsupervised)
German (translation)
Tags (parsing)English
One-to-many
English (unsupervised)
Image (captioning) English
German (translation)
Many-to-one
German (translation)
English (unsupervised) German (unsupervised)
English
Many-to-many
Able to improve accuracies of all related tasks
English (unsupervised)
German (translation)
Tags (parsing)English
One-to-many
English (unsupervised)
Image (captioning) English
German (translation)
Many-to-one
German (translation)
English (unsupervised) German (unsupervised)
English
Many-to-many
Dai, Le. Semi-supervised Sequence Learning. NIPS, 2015. Luong et al. Multitask Sequence Learning. ICLR, 2016
Able to improve accuracies of all related tasks
Machines that understand natural language
Step 1: Understand words
Step 2: Understand strings of words
Step 3: Have memory, logical reasoning
Step 4: Learn from many tasks
