The document addresses the challenge of handling rare and unknown words in natural language processing (NLP) systems, proposing a novel solution using neural networks and attention mechanisms. The model features two softmax layers to predict outputs from a predefined shortlist of the most frequent words, while unknown words are represented by a special token. The authors highlight fundamental issues with the shortlist approach and discuss the architecture of the proposed neural machine translation model.

1. Pointing
the Unknown Words
1
ACL 2016
Pointing the Unknown Words
Caglar Gulcehre
Universit´e de Montr´eal
Sungjin Ahn
Universit´e de Montr´eal
Ramesh Nallapati
IBM T.J. Watson Research
Bowen Zhou
IBM T.J. Watson Research
Yoshua Bengio
Universit´e de Montr´eal
CIFAR Senior Fellow
Abstract
The problem of rare and unknown words
is an important issue that can potentially
effect the performance of many NLP sys-
tems, including both the traditional count-
based and the deep learning models. We
propose a novel way to deal with the rare
and unseen words for the neural network
models using attention. Our model uses
two softmax layers in order to predict the
softmax output layer where each of the output di-
mension corresponds to a word in a predefined
word-shortlist. Because computing high dimen-
sional softmax is computationally expensive, in
practice the shortlist is limited to have only top-
K most frequent words in the training corpus. All
other words are then replaced by a special word,
called the unknown word (UNK).
The shortlist approach has two fundamental
problems. The first problem, which is known as
21Aug2016
Pointing the Unknown Words
Caglar Gulcehre
Universit´e de Montr´eal
Sungjin Ahn
Universit´e de Montr´eal
Ramesh Nallapati
IBM T.J. Watson Research
Bowen Zhou
IBM T.J. Watson Research
Yoshua Bengio
Universit´e de Montr´eal
CIFAR Senior Fellow
Abstract
The problem of rare and unknown words
is an important issue that can potentially
effect the performance of many NLP sys-
tems, including both the traditional count-
based and the deep learning models. We
propose a novel way to deal with the rare
and unseen words for the neural network
models using attention. Our model uses
two softmax layers in order to predict the
softmax output layer where each of the output di-
mension corresponds to a word in a predefined
word-shortlist. Because computing high dimen-
sional softmax is computationally expensive, in
practice the shortlist is limited to have only top-
K most frequent words in the training corpus. All
other words are then replaced by a special word,
called the unknown word (UNK).
The shortlist approach has two fundamental
problems. The first problem, which is known as
the rare word problem, is that some of the words
21Aug2016

2. 2
-
e
-
-
e
-
f
e
l
o
-
Guillaume et Cesar ont une voiture bleue a Lausanne.
Guillaume and Cesar have a blue car in Lausanne.
Copy Copy Copy
French:
English:
Figure 1: An example of how copying can happen
for machine translation. Common words that ap-
pear both in source and the target can directly be
copied from input to source. The rest of the un-
known in the target can be copied from the input
after being translated with a dictionary.

3. •
•
•
• V
a man yesterday . [eos]!
killed a man yesterday .!t
+ bhp) (16)
(17)
Whp
bhp) (16)
(17)
(18)
(19)
!
man
a
a
man !
6
RNN t
pt
pt = softmax(W
ht =
−−−→
RNNt′≺t(
softmax(s)i =
exp(si
sj∈s exp
→
t′≺t(xwt′ ) (17)
p(si)
s exp(sj)
(18)
(19)
s i
Whp ∈ RV ×N
V
bhp(w)
Whp bhp
t = softmax(Whpht + bhp) (16)
t =
−−−→
RNNt′≺t(xwt′ ) (17)
i =
exp(si)
sj∈s exp(sj)
(18)
(19)
s i
Whp ∈ RV ×N
V
Whp(w) bhp(w)
(18)
(19)
i
Whp ∈ RV ×N
V
)
t wt
pt = softmax(Whpht + bhp)
ht =
−−−→
RNNt′≺t(xwt′ )
6
RNN t wt
pt
pt = softmax(Whpht + bhp)
ht =
−−−→
RNNt′≺t(xwt′ )
softmax(s)i =
exp(si)
sj∈s exp(sj)
softmax(s)i N s
exp 3

4. •
•
•
• V
a man yesterday . [eos]!
killed a man yesterday .!t
+ bhp) (16)
(17)
Whp
bhp) (16)
(17)
(18)
(19)
!
man
a
a
man !
6
RNN t
pt
pt = softmax(W
ht =
−−−→
RNNt′≺t(
softmax(s)i =
exp(si
sj∈s exp
→
t′≺t(xwt′ ) (17)
p(si)
s exp(sj)
(18)
(19)
s i
Whp ∈ RV ×N
V
bhp(w)
Whp bhp
t = softmax(Whpht + bhp) (16)
t =
−−−→
RNNt′≺t(xwt′ ) (17)
i =
exp(si)
sj∈s exp(sj)
(18)
(19)
s i
Whp ∈ RV ×N
V
Whp(w) bhp(w)
(18)
(19)
i
Whp ∈ RV ×N
V
)
t wt
pt = softmax(Whpht + bhp)
ht =
−−−→
RNNt′≺t(xwt′ )
• V T
•
•
•
Pointer Softmax
pt = softmax(Whpht + bhp)
ht =
−−−→
RNNt′≺t(xwt′ )
softmax(s)i =
exp(si)
sj∈s exp(sj)
i N s
exp
w Whp ∈ RV ×N
Whp(w) bhp(w)
T
6
RNN t wt
pt
pt = softmax(Whpht + bhp)
ht =
−−−→
RNNt′≺t(xwt′ )
softmax(s)i =
exp(si)
sj∈s exp(sj)
softmax(s)i N s
exp
6
RNN t wt
pt
pt = softmax(Whpht +
ht =
−−−→
RNNt′≺t(xwt′ )
softmax(s)i =
exp(si)
sj∈s exp(sj)
softmax(s)i N s
exp
・
4

5. •
5
rce sentence during decoding a translation (Sec. 3.1).
ER: GENERAL DESCRIPTION
x1 x2 x3 xT
+
αt,1
αt,2 αt,3
αt,T
h1 h2 h3 hT
h1 h2 h3 hT
st-1 st
Figure 1: The graphical illus-
tration of the proposed model
trying to generate the t-th tar-
get word yt given a source
sentence (x , x , . . . , x ).
el architecture, we define each conditional probability
p(yi|y1, . . . , yi 1, x) = g(yi 1, si, ci), (4)
RNN hidden state for time i, computed by
si = f(si 1, yi 1, ci).
noted that unlike the existing encoder–decoder ap-
q. (2)), here the probability is conditioned on a distinct
ci for each target word yi.
vector ci depends on a sequence of annotations
) to which an encoder maps the input sentence. Each
contains information about the whole input sequence
focus on the parts surrounding the i-th word of the
e. We explain in detail how the annotations are com-
ext section.
ector ci is, then, computed as a weighted sum of these
i:
TxX
F
t
t
g
s
t vector ci depends on a sequence of annotations
x
) to which an encoder maps the input sentence. Each
hi contains information about the whole input sequence
g focus on the parts surrounding the i-th word of the
nce. We explain in detail how the annotations are com-
next section.
vector ci is, then, computed as a weighted sum of these
hi:
ci =
TxX
j=1
↵ijhj. (5)
↵ij of each annotation hj is computed by
↵ij =
exp (eij)
P ,
GENERAL DESCRIPTION
hitecture, we define each conditional probability
y1, . . . , yi 1, x) = g(yi 1, si, ci), (4)
N hidden state for time i, computed by
si = f(si 1, yi 1, ci).
d that unlike the existing encoder–decoder ap-
), here the probability is conditioned on a distinct
or each target word yi.
or ci depends on a sequence of annotations
which an encoder maps the input sentence. Each
Figure 1: The graphical illus-
tration of the proposed model
trying to generate the t-th tar-
get word yt given a source
sentence (x1, x2, . . . , xT ).
ere the probability is conditioned on a distinct
ach target word yi.
ci depends on a sequence of annotations
ch an encoder maps the input sentence. Each
s information about the whole input sequence
n the parts surrounding the i-th word of the
xplain in detail how the annotations are com-
on.
is, then, computed as a weighted sum of these
ci =
TxX
j=1
↵ijhj. (5)
h annotation hj is computed by
↵ij =
exp (eij)
PTx
k=1 exp (eik)
, (6)
e = a(s , h )
Figure 1: The graphical illus-
tration of the proposed model
trying to generate the t-th tar-
get word yt given a source
sentence (x1, x2, . . . , xT ).
t the whole input sequence
nding the i-th word of the
w the annotations are com-
as a weighted sum of these
. (5)
computed by
j =
exp (eij)
PTx
k=1 exp (eik)
, (6)
eij = a(si 1, hj)
well the inputs around position j and the output at position
hidden state si 1 (just before emitting yi, Eq. (4)) and the
3 Neural Machine Translation Model
with Attention
As the baseline neural machine translation sys-
tem, we use the model proposed by (Bahdanau et
al., 2014) that learns to (soft-)align and translate
jointly. We refer this model as NMT.
The encoder of the NMT is a bidirectional
RNN (Schuster and Paliwal, 1997). The forward
RNN reads input sequence x = (x1, . . . , xT )
in left-to-right direction, resulting in a sequence
of hidden states (
!
h 1, . . . ,
!
h T ). The backward
RNN reads x in the reversed direction and outputs
( h 1, . . . , h T ). We then concatenate the hidden
states of forward and backward RNNs at each time
step and obtain a sequence of annotation vectors
(h1, . . . , hT ) where hj =
h!
h j|| h j
i
. Here, ||
denotes the concatenation operator. Thus, each an-
notation vector hj encodes information about the
j-th word with respect to all the other surrounding
where fr is G
We use a
2013) to com
words:
p(yt
ex
where W is
bias of the o
forward neu
that perform
And the sup
umn vector o
The whol
and the deco
(conditional
p(yi|y1, . . . , yi 1, x) = g(yi 1, si, ci),
is an RNN hidden state for time i, computed by
si = f(si 1, yi 1, ci).
be noted that unlike the existing encoder–decoder
ee Eq. (2)), here the probability is conditioned on a dis
ector ci for each target word yi.
ext vector ci depends on a sequence of annotat
hTx
) to which an encoder maps the input sentence. E
n hi contains information about the whole input sequ
ong focus on the parts surrounding the i-th word of
uence. We explain in detail how the annotations are c
(t=i)
[Bahdanau+15]
ncepaperatICLR2015
trainedtopredictthenextwordyt0giventhecontextvectorcandallthe
ords{y1,···,yt01}.Inotherwords,thedecoderdefinesaprobabilityover
composingthejointprobabilityintotheorderedconditionals:
p(y)=
TY
t=1
p(yt|{y1,···,yt1},c),(2)
Ty
.WithanRNN,eachconditionalprobabilityismodeledas
p(yt|{y1,···,yt1},c)=g(yt1,st,c),(3)
potentiallymulti-layered,functionthatoutputstheprobabilityofyt,andstis
RNN.ItshouldbenotedthatotherarchitecturessuchasahybridofanRNN
lneuralnetworkcanbeused(KalchbrennerandBlunsom,2013).
ALIGNANDTRANSLATE
poseanovelarchitectureforneuralmachinetranslation.Thenewarchitecture
onalRNNasanencoder(Sec.3.2)andadecoderthatemulatessearching
nceduringdecodingatranslation(Sec.3.1).
ERALDESCRIPTION
st
cture,wedefineeachconditionalprobability
...,yi1,x)=g(yi1,si,ci),(4)
iddenstatefortimei,computedby
si=f(si1,yi1,ci).
atunliketheexistingencoder–decoderap-
eretheprobabilityisconditionedonadistinct
achtargetwordyi.
idependsonasequenceofannotations
nedtopredictthenextwordyt0giventhecontextvectorcandallthe
s{y1,···,yt01}.Inotherwords,thedecoderdefinesaprobabilityover
posingthejointprobabilityintotheorderedconditionals:
p(y)=
TY
t=1
p(yt|{y1,···,yt1},c),(2)
.WithanRNN,eachconditionalprobabilityismodeledas
p(yt|{y1,···,yt1},c)=g(yt1,st,c),(3)
entiallymulti-layered,functionthatoutputstheprobabilityofyt,andstis
N.ItshouldbenotedthatotherarchitecturessuchasahybridofanRNN
uralnetworkcanbeused(KalchbrennerandBlunsom,2013).
LIGNANDTRANSLATE
anovelarchitectureforneuralmachinetranslation.Thenewarchitecture
lRNNasanencoder(Sec.3.2)andadecoderthatemulatessearching
duringdecodingatranslation(Sec.3.1).
ALDESCRIPTION
st
e,wedefineeachconditionalprobability
,yi1,x)=g(yi1,si,ci),(4)
nstatefortimei,computedby
f(si1,yi1,ci).
nliketheexistingencoder–decoderap-
heprobabilityisconditionedonadistinct
targetwordyi.
ependsonasequenceofannotations
encodermapstheinputsentence.Each
ormationaboutthewholeinputsequence
epartssurroundingthei-thwordofthe
inindetailhowtheannotationsarecom-
atICLR2015
predictthenextwordyt0giventhecontextvectorcandallthe
···,yt01}.Inotherwords,thedecoderdefinesaprobabilityover
thejointprobabilityintotheorderedconditionals:
y)=
TY
t=1
p(yt|{y1,···,yt1},c),(2)
anRNN,eachconditionalprobabilityismodeledas
|{y1,···,yt1},c)=g(yt1,st,c),(3)
multi-layered,functionthatoutputstheprobabilityofyt,andstis
ouldbenotedthatotherarchitecturessuchasahybridofanRNN
tworkcanbeused(KalchbrennerandBlunsom,2013).
ANDTRANSLATE
larchitectureforneuralmachinetranslation.Thenewarchitecture
asanencoder(Sec.3.2)andadecoderthatemulatessearching
decodingatranslation(Sec.3.1).
SCRIPTION
st
efineeachconditionalprobability
x)=g(yi1,si,ci),(4)
fortimei,computedby
1,yi1,ci).
theexistingencoder–decoderap-
abilityisconditionedonadistinct
ordyi.
onasequenceofannotations
dictthenextwordyt0giventhecontextvectorcandallthe
,yt01}.Inotherwords,thedecoderdefinesaprobabilityover
ejointprobabilityintotheorderedconditionals:
=
TY
t=1
p(yt|{y1,···,yt1},c),(2)
NN,eachconditionalprobabilityismodeledas
y1,···,yt1},c)=g(yt1,st,c),(3)
ulti-layered,functionthatoutputstheprobabilityofyt,andstis
ldbenotedthatotherarchitecturessuchasahybridofanRNN
rkcanbeused(KalchbrennerandBlunsom,2013).
DTRANSLATE
rchitectureforneuralmachinetranslation.Thenewarchitecture
anencoder(Sec.3.2)andadecoderthatemulatessearching
codingatranslation(Sec.3.1).
IPTION
st
neeachconditionalprobability
g(yi1,si,ci),(4)
timei,computedby
i1,ci).
existingencoder–decoderap-
ilityisconditionedonadistinct
dyi.
nasequenceofannotations
mapstheinputsentence.Each
boutthewholeinputsequence
rroundingthei-thwordofthe
lhowtheannotationsarecom-
p(yt | {y1, · · · , yt 1} , c) = g(yt 1, st, c), (3)
where g is a nonlinear, potentially multi-layered, function that outputs the probability of yt, and st is
the hidden state of the RNN. It should be noted that other architectures such as a hybrid of an RNN
and a de-convolutional neural network can be used (Kalchbrenner and Blunsom, 2013).
3 LEARNING TO ALIGN AND TRANSLATE
In this section, we propose a novel architecture for neural machine translation. The new architecture
consists of a bidirectional RNN as an encoder (Sec. 3.2) and a decoder that emulates searching
through a source sentence during decoding a translation (Sec. 3.1).
3.1 DECODER: GENERAL DESCRIPTION
yt-1
Figure 1: The graphical illus-
tration of the proposed model
trying to generate the t-th tar-
get word yt given a source
sentence (x1, x2, . . . , xT ).
In a new model architecture, we define each conditional probability
in Eq. (2) as:
p(yi|y1, . . . , yi 1, x) = g(yi 1, si, ci), (4)
where si is an RNN hidden state for time i, computed by
si = f(si 1, yi 1, ci).
It should be noted that unlike the existing encoder–decoder ap-
proach (see Eq. (2)), here the probability is conditioned on a distinct
context vector ci for each target word yi.
The context vector ci depends on a sequence of annotations
(h1, · · · , hTx
) to which an encoder maps the input sentence. Each
annotation hi contains information about the whole input sequence
with a strong focus on the parts surrounding the i-th word of the
input sequence. We explain in detail how the annotations are com-
puted in the next section.
The context vector ci is, then, computed as a weighted sum of these
annotations hi:
ci =
TxX
j=1
↵ijhj. (5)
The weight ↵ij of each annotation hj is computed by
↵ij =
exp (eij)
PTx
exp (eik)
, (6)
ptpt-1
c.f. http://www.slideshare.net/yutakikuchi927/deep-learning-nlp-attention

6. •
•
α
6
rce sentence during decoding a translation (Sec. 3.1).
ER: GENERAL DESCRIPTION
x1 x2 x3 xT
+
αt,1
αt,2 αt,3
αt,T
h1 h2 h3 hT
h1 h2 h3 hT
st-1 st
Figure 1: The graphical illus-
tration of the proposed model
trying to generate the t-th tar-
get word yt given a source
sentence (x , x , . . . , x ).
el architecture, we define each conditional probability
p(yi|y1, . . . , yi 1, x) = g(yi 1, si, ci), (4)
RNN hidden state for time i, computed by
si = f(si 1, yi 1, ci).
noted that unlike the existing encoder–decoder ap-
q. (2)), here the probability is conditioned on a distinct
ci for each target word yi.
vector ci depends on a sequence of annotations
) to which an encoder maps the input sentence. Each
contains information about the whole input sequence
focus on the parts surrounding the i-th word of the
e. We explain in detail how the annotations are com-
ext section.
ector ci is, then, computed as a weighted sum of these
i:
TxX
F
t
t
g
s
t vector ci depends on a sequence of annotations
x
) to which an encoder maps the input sentence. Each
hi contains information about the whole input sequence
g focus on the parts surrounding the i-th word of the
nce. We explain in detail how the annotations are com-
next section.
vector ci is, then, computed as a weighted sum of these
hi:
ci =
TxX
j=1
↵ijhj. (5)
↵ij of each annotation hj is computed by
↵ij =
exp (eij)
P ,
GENERAL DESCRIPTION
hitecture, we define each conditional probability
y1, . . . , yi 1, x) = g(yi 1, si, ci), (4)
N hidden state for time i, computed by
si = f(si 1, yi 1, ci).
d that unlike the existing encoder–decoder ap-
), here the probability is conditioned on a distinct
or each target word yi.
or ci depends on a sequence of annotations
which an encoder maps the input sentence. Each
Figure 1: The graphical illus-
tration of the proposed model
trying to generate the t-th tar-
get word yt given a source
sentence (x1, x2, . . . , xT ).
ere the probability is conditioned on a distinct
ach target word yi.
ci depends on a sequence of annotations
ch an encoder maps the input sentence. Each
s information about the whole input sequence
n the parts surrounding the i-th word of the
xplain in detail how the annotations are com-
on.
is, then, computed as a weighted sum of these
ci =
TxX
j=1
↵ijhj. (5)
h annotation hj is computed by
↵ij =
exp (eij)
PTx
k=1 exp (eik)
, (6)
e = a(s , h )
Figure 1: The graphical illus-
tration of the proposed model
trying to generate the t-th tar-
get word yt given a source
sentence (x1, x2, . . . , xT ).
t the whole input sequence
nding the i-th word of the
w the annotations are com-
as a weighted sum of these
. (5)
computed by
j =
exp (eij)
PTx
k=1 exp (eik)
, (6)
eij = a(si 1, hj)
well the inputs around position j and the output at position
hidden state si 1 (just before emitting yi, Eq. (4)) and the
3 Neural Machine Translation Model
with Attention
As the baseline neural machine translation sys-
tem, we use the model proposed by (Bahdanau et
al., 2014) that learns to (soft-)align and translate
jointly. We refer this model as NMT.
The encoder of the NMT is a bidirectional
RNN (Schuster and Paliwal, 1997). The forward
RNN reads input sequence x = (x1, . . . , xT )
in left-to-right direction, resulting in a sequence
of hidden states (
!
h 1, . . . ,
!
h T ). The backward
RNN reads x in the reversed direction and outputs
( h 1, . . . , h T ). We then concatenate the hidden
states of forward and backward RNNs at each time
step and obtain a sequence of annotation vectors
(h1, . . . , hT ) where hj =
h!
h j|| h j
i
. Here, ||
denotes the concatenation operator. Thus, each an-
notation vector hj encodes information about the
j-th word with respect to all the other surrounding
where fr is G
We use a
2013) to com
words:
p(yt
ex
where W is
bias of the o
forward neu
that perform
And the sup
umn vector o
The whol
and the deco
(conditional
(t=i)
ncepaperatICLR2015
trainedtopredictthenextwordyt0giventhecontextvectorcandallthe
ords{y1,···,yt01}.Inotherwords,thedecoderdefinesaprobabilityover
composingthejointprobabilityintotheorderedconditionals:
p(y)=
TY
t=1
p(yt|{y1,···,yt1},c),(2)
Ty
.WithanRNN,eachconditionalprobabilityismodeledas
p(yt|{y1,···,yt1},c)=g(yt1,st,c),(3)
potentiallymulti-layered,functionthatoutputstheprobabilityofyt,andstis
RNN.ItshouldbenotedthatotherarchitecturessuchasahybridofanRNN
lneuralnetworkcanbeused(KalchbrennerandBlunsom,2013).
ALIGNANDTRANSLATE
poseanovelarchitectureforneuralmachinetranslation.Thenewarchitecture
onalRNNasanencoder(Sec.3.2)andadecoderthatemulatessearching
nceduringdecodingatranslation(Sec.3.1).
ERALDESCRIPTION
st
cture,wedefineeachconditionalprobability
...,yi1,x)=g(yi1,si,ci),(4)
iddenstatefortimei,computedby
si=f(si1,yi1,ci).
atunliketheexistingencoder–decoderap-
eretheprobabilityisconditionedonadistinct
achtargetwordyi.
idependsonasequenceofannotations
nedtopredictthenextwordyt0giventhecontextvectorcandallthe
s{y1,···,yt01}.Inotherwords,thedecoderdefinesaprobabilityover
posingthejointprobabilityintotheorderedconditionals:
p(y)=
TY
t=1
p(yt|{y1,···,yt1},c),(2)
.WithanRNN,eachconditionalprobabilityismodeledas
p(yt|{y1,···,yt1},c)=g(yt1,st,c),(3)
entiallymulti-layered,functionthatoutputstheprobabilityofyt,andstis
N.ItshouldbenotedthatotherarchitecturessuchasahybridofanRNN
uralnetworkcanbeused(KalchbrennerandBlunsom,2013).
LIGNANDTRANSLATE
anovelarchitectureforneuralmachinetranslation.Thenewarchitecture
lRNNasanencoder(Sec.3.2)andadecoderthatemulatessearching
duringdecodingatranslation(Sec.3.1).
ALDESCRIPTION
st
e,wedefineeachconditionalprobability
,yi1,x)=g(yi1,si,ci),(4)
nstatefortimei,computedby
f(si1,yi1,ci).
nliketheexistingencoder–decoderap-
heprobabilityisconditionedonadistinct
targetwordyi.
ependsonasequenceofannotations
encodermapstheinputsentence.Each
ormationaboutthewholeinputsequence
epartssurroundingthei-thwordofthe
inindetailhowtheannotationsarecom-
atICLR2015
predictthenextwordyt0giventhecontextvectorcandallthe
···,yt01}.Inotherwords,thedecoderdefinesaprobabilityover
thejointprobabilityintotheorderedconditionals:
y)=
TY
t=1
p(yt|{y1,···,yt1},c),(2)
anRNN,eachconditionalprobabilityismodeledas
|{y1,···,yt1},c)=g(yt1,st,c),(3)
multi-layered,functionthatoutputstheprobabilityofyt,andstis
ouldbenotedthatotherarchitecturessuchasahybridofanRNN
tworkcanbeused(KalchbrennerandBlunsom,2013).
ANDTRANSLATE
larchitectureforneuralmachinetranslation.Thenewarchitecture
asanencoder(Sec.3.2)andadecoderthatemulatessearching
decodingatranslation(Sec.3.1).
SCRIPTION
st
efineeachconditionalprobability
x)=g(yi1,si,ci),(4)
fortimei,computedby
1,yi1,ci).
theexistingencoder–decoderap-
abilityisconditionedonadistinct
ordyi.
onasequenceofannotations
dictthenextwordyt0giventhecontextvectorcandallthe
,yt01}.Inotherwords,thedecoderdefinesaprobabilityover
ejointprobabilityintotheorderedconditionals:
=
TY
t=1
p(yt|{y1,···,yt1},c),(2)
NN,eachconditionalprobabilityismodeledas
y1,···,yt1},c)=g(yt1,st,c),(3)
ulti-layered,functionthatoutputstheprobabilityofyt,andstis
ldbenotedthatotherarchitecturessuchasahybridofanRNN
rkcanbeused(KalchbrennerandBlunsom,2013).
DTRANSLATE
rchitectureforneuralmachinetranslation.Thenewarchitecture
anencoder(Sec.3.2)andadecoderthatemulatessearching
codingatranslation(Sec.3.1).
IPTION
st
neeachconditionalprobability
g(yi1,si,ci),(4)
timei,computedby
i1,ci).
existingencoder–decoderap-
ilityisconditionedonadistinct
dyi.
nasequenceofannotations
mapstheinputsentence.Each
boutthewholeinputsequence
rroundingthei-thwordofthe
lhowtheannotationsarecom-
p(yt | {y1, · · · , yt 1} , c) = g(yt 1, st, c), (3)
where g is a nonlinear, potentially multi-layered, function that outputs the probability of yt, and st is
the hidden state of the RNN. It should be noted that other architectures such as a hybrid of an RNN
and a de-convolutional neural network can be used (Kalchbrenner and Blunsom, 2013).
3 LEARNING TO ALIGN AND TRANSLATE
In this section, we propose a novel architecture for neural machine translation. The new architecture
consists of a bidirectional RNN as an encoder (Sec. 3.2) and a decoder that emulates searching
through a source sentence during decoding a translation (Sec. 3.1).
3.1 DECODER: GENERAL DESCRIPTION
yt-1
Figure 1: The graphical illus-
tration of the proposed model
trying to generate the t-th tar-
get word yt given a source
sentence (x1, x2, . . . , xT ).
In a new model architecture, we define each conditional probability
in Eq. (2) as:
p(yi|y1, . . . , yi 1, x) = g(yi 1, si, ci), (4)
where si is an RNN hidden state for time i, computed by
si = f(si 1, yi 1, ci).
It should be noted that unlike the existing encoder–decoder ap-
proach (see Eq. (2)), here the probability is conditioned on a distinct
context vector ci for each target word yi.
The context vector ci depends on a sequence of annotations
(h1, · · · , hTx
) to which an encoder maps the input sentence. Each
annotation hi contains information about the whole input sequence
with a strong focus on the parts surrounding the i-th word of the
input sequence. We explain in detail how the annotations are com-
puted in the next section.
The context vector ci is, then, computed as a weighted sum of these
annotations hi:
ci =
TxX
j=1
↵ijhj. (5)
The weight ↵ij of each annotation hj is computed by
↵ij =
exp (eij)
PTx
exp (eik)
, (6)
ptpt-1

7. •
•
•
• V
a man yesterday . [eos]!
killed a man yesterday .!t
+ bhp) (16)
(17)
Whp
bhp) (16)
(17)
(18)
(19)
!
man
a
a
man !
6
RNN t
pt
pt = softmax(W
ht =
−−−→
RNNt′≺t(
softmax(s)i =
exp(si
sj∈s exp
→
t′≺t(xwt′ ) (17)
p(si)
s exp(sj)
(18)
(19)
s i
Whp ∈ RV ×N
V
bhp(w)
Whp bhp
t = softmax(Whpht + bhp) (16)
t =
−−−→
RNNt′≺t(xwt′ ) (17)
i =
exp(si)
sj∈s exp(sj)
(18)
(19)
s i
Whp ∈ RV ×N
V
Whp(w) bhp(w)
(18)
(19)
i
Whp ∈ RV ×N
V
)
t wt
pt = softmax(Whpht + bhp)
ht =
−−−→
RNNt′≺t(xwt′ )
• V T
•
•
•
pt = softmax(Whpht + bhp)
ht =
−−−→
RNNt′≺t(xwt′ )
softmax(s)i =
exp(si)
sj∈s exp(sj)
i N s
exp
w Whp ∈ RV ×N
Whp(w) bhp(w)
T
6
RNN t wt
pt
pt = softmax(Whpht + bhp)
ht =
−−−→
RNNt′≺t(xwt′ )
softmax(s)i =
exp(si)
sj∈s exp(sj)
softmax(s)i N s
exp
6
RNN t wt
pt
pt = softmax(Whpht +
ht =
−−−→
RNNt′≺t(xwt′ )
softmax(s)i =
exp(si)
sj∈s exp(sj)
softmax(s)i N s
exp
・
7

8. •
8
atten-
nd the
e pro-
or un-
at we
Guillaume et Cesar ont une voiture bleue a Lausanne.
Guillaume and Cesar have a blue car in Lausanne.
Copy Copy Copy
French:
English:
Figure 1: An example of how copying can happen

9. •
•
•
•
9

10. •
10
Table 4: Generated summaries from NMT with PS. Boldface words are the words copied from the source
Source #1 china ’s tang gonghong set a world record with a clean and
jerk lift of ### kilograms to win the women ’s over-## kilogram
weightlifting title at the asian games on tuesday .
Target #1 china ’s tang <unk>,sets world weightlifting record
NMT+PS #1 china ’s tang gonghong wins women ’s weightlifting weightlift-
ing title at asian games
Source #2 owing to criticism , nbc said on wednesday that it was ending
a three-month-old experiment that would have brought the first
liquor advertisements onto national broadcast network television
Table 4: Generated summaries from NMT with PS. Boldface words are the words copied from the so
Source #1 china ’s tang gonghong set a world record with a clean and
jerk lift of ### kilograms to win the women ’s over-## kilogram
weightlifting title at the asian games on tuesday .
Target #1 china ’s tang <unk>,sets world weightlifting record
NMT+PS #1 china ’s tang gonghong wins women ’s weightlifting weightlift-
<v1> ’s <v2> <v3> set a world record with a clean and
jerk lift of ### kilograms to win the women ’s over-##
kilogram weightlifting title at the asian games on tuesday .
<v1> ’s <v2> <v3>,sets world weightlifting record

11. •
•
•
•
•
•
•
11

12. • gonghonh <unk>
•
•
12
The experimental results comparing the Pointer
Softmax with NMT model are displayed in Ta-
ble 1 for the UNK pointers data and in Table 2
for the entity pointers data. As our experiments
show, pointer softmax improves over the baseline
NMT on both UNK data and entities data. Our
hope was that the improvement would be larger
for the entities data since the incidence of point-
ers was much greater. However, it turns out this
is not the case, and we suspect the main reason
is anonymization of entities which removed data-
sparsity by converting all entities to integer-ids
that are shared across all documents. We believe
that on de-anonymized data, our model could help
more, since the issue of data-sparsity is more acute
in this case.
Table 1: Results on Gigaword Corpus when point-
ers are used for UNKs in the training data, using
Rouge-F1 as the evaluation metric.
Rouge-1 Rouge-2 Rouge-L
NMT + lvt 34.87 16.54 32.27
NMT + lvt + PS 35.19 16.66 32.51
I
ate
tra
am
acc
it n
5.3
In
me
the
the
Fre
ear
log
eva
els
sco
W
the
tok
Th
gli
We
Table 4: Generated summaries from NMT with PS. Boldface words are the words copied from the source
Source #1 china ’s tang gonghong set a world record with a clean and
jerk lift of ### kilograms to win the women ’s over-## kilogram
weightlifting title at the asian games on tuesday .
Target #1 china ’s tang <unk>,sets world weightlifting record
NMT+PS #1 china ’s tang gonghong wins women ’s weightlifting weightlift-
ing title at asian games
Source #2 owing to criticism , nbc said on wednesday that it was ending
a three-month-old experiment that would have brought the first
liquor advertisements onto national broadcast network television
.
Target #2 advertising : nbc retreats from liquor commercials
NMT+PS #2 nbc says it is ending a three-month-old experiment
Source #3 a senior trade union official here wednesday called on ghana ’s
government to be “ mindful of the plight ” of the ordinary people
in the country in its decisions on tax increases .
Target #3 tuc official,on behalf of ordinary ghanaians
NMT+PS #3 ghana ’s government urged to be mindful of the plight

13. •
•
13
is not the case, and we suspect the main reason
is anonymization of entities which removed data-
sparsity by converting all entities to integer-ids
that are shared across all documents. We believe
that on de-anonymized data, our model could help
more, since the issue of data-sparsity is more acute
in this case.
Table 1: Results on Gigaword Corpus when point-
ers are used for UNKs in the training data, using
Rouge-F1 as the evaluation metric.
Rouge-1 Rouge-2 Rouge-L
NMT + lvt 34.87 16.54 32.27
NMT + lvt + PS 35.19 16.66 32.51
Table 2: Results on anonymized Gigaword Corpus
when pointers are used for entities, using Rouge-
F1 as the evaluation metric.
Rouge-1 Rouge-2 Rouge-L
NMT + lvt 34.89 16.78 32.37
NMT + lvt + PS 35.11 16.76 32.55
Table 4: Generated summaries from NMT with PS. Boldface words are the words copied from the source
Source #1 china ’s tang gonghong set a world record with a clean and
jerk lift of ### kilograms to win the women ’s over-## kilogram
weightlifting title at the asian games on tuesday .
Target #1 china ’s tang <unk>,sets world weightlifting record
NMT+PS #1 china ’s tang gonghong wins women ’s weightlifting weightlift-
ing title at asian games
Source #2 owing to criticism , nbc said on wednesday that it was ending
a three-month-old experiment that would have brought the first
liquor advertisements onto national broadcast network television
.
Target #2 advertising : nbc retreats from liquor commercials
NMT+PS #2 nbc says it is ending a three-month-old experiment
Source #3 a senior trade union official here wednesday called on ghana ’s
government to be “ mindful of the plight ” of the ordinary people
<v1> ’s <v2> <v3> set a world record with a clean and
jerk lift of ### kilograms to win the women ’s over-##
kilogram weightlifting title at the asian games on tuesday .
<v1> ’s <v2> <v3>,sets world weightlifting record

14. •
•
•
•
•
•
• 14
en-
the
pro-
un-
we
ict-
e of
We
odel
Guillaume et Cesar ont une voiture bleue a Lausanne.
Guillaume and Cesar have a blue car in Lausanne.
Copy Copy Copy
French:
English:
Figure 1: An example of how copying can happen
for machine translation. Common words that ap-
pear both in source and the target can directly be
copied from input to source. The rest of the un-
known in the target can be copied from the input
after being translated with a dictionary.

15. •
•
•
•
15
of the gradients exceed 1 (Pascanu et al., 2012).
Table 5: Europarl Dataset (EN-FR)
BLEU-4
NMT 20.19
NMT + PS 23.76
in the country in its decisions on tax increases .
et #3 tuc official,on behalf of ordinary ghanaians
T+PS #3 ghana ’s government urged to be mindful of the plight
y, we first check if the same word yt ap-
e source sentence. If it is not, we then
translated version of the word exists in
sentence by using a look-up table be-
source and the target language. If the
the source sentence, we then use the lo-
he word in the source as the target. Oth-
check if one of the English senses from
anguage dictionary of the French word
urce. If it is in the source sentence, then
location of that word as our translation.
we just use the argmax of lt as the tar-
ching network dt, we observed that us-
layered MLP with noisy-tanh activation
et al., 2016) function with residual con-
om the lower layer (He et al., 2015) ac-
In Table 5, we provided the result of NMT w
pointer softmax and we observe about 3.6 BLE
score improvement over our baseline.
Figure 4: A comparison of the validation learnin

16. •
•
•
16
h2 hTh1 …
yt-1
Source Sequence
x2 xTx1 …
BiRNN
Target Sequence
Figure 2: A depiction of neural machine transla-
tion architecture with attention. At each timestep,
the model generates the attention distribution lt.
We use lt and the encoder’s hidden states to obtain
the context ct. The decoder uses ct to predict a
vector of probabilities for the words wt by using
vocabulary softmax.
4 The Pointer Softmax
In this section, we introduce our method, called as
the pointer softmax (PS), to deal with the rare and
unknown words. The pointer softmax can be an
applicable approach to many NLP tasks, because
it resolves the limitations about unknown words
for neural networks. It can be used in parallel with
other existing techniques such as the large vocabu-
lary trick (Jean et al., 2014). Our model learns two
key abilities jointly to make the pointing mech-
anism applicable in more general settings: (i) to
predict whether it is required to use the pointing
complish this, we introduce a switching network
to the model. The switching network, which is
a multilayer perceptron in our experiments, takes
the representation of the context sequence (similar
to the input annotation in NMT) and the previous
hidden state of the output RNN as its input. It out-
puts a binary variable zt which indicates whether
to use the shortlist softmax (when zt = 1) or the
location softmax (when zt = 0). Note that if the
word that is expected to be generated at each time-
step is neither in the shortlist nor in the context se-
quence, the switching network selects the shortlist
softmax, and then the shortlist softmax predicts
UNK. The details of the pointer softmax model can
be seen in Figure 3 as well.
h2 hTh1 …
st ct
zt yl
tyw
t
yt-1
Vocabulary softmax
Pointer distribution (lt)
Source Sequence
Point & copy
x2 xTx1 …
BiRNN
Target Sequence
p 1-p
st-1
Figure 3: A depiction of the Pointer Softmax (PS)

17. Ø
Ø
Ø
Ø
Ø
Ø
17
improved the convergence speed of the model as
well. For French to English machine translation
on Europarl corpora, we observe that using the
pointer softmax can also improve the training con-
vergence of the model.
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http://deeplearning.net/software/
theano/
149
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pointer softmax can also improve the training con-
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7 Acknowledgments
We would also like to thank the developers of
5
ranslation
using the
ining con-
Kyunghyun
al machine
d translate.
and Jean-
importance
ural proba-
orks, IEEE
as Usunier,
5. Large-
emory net-
nd Mirella
on by ex-
Machine Learning Research, 13(1):307–361.
[He et al.2015] Kaiming He, Xiangyu Zhang, Shao-
qing Ren, and Jian Sun. 2015. Deep resid-
ual learning for mage recognition. arXiv preprint
arXiv:1512.03385.
[Hermann et al.2015] Karl Moritz Hermann, Tomas
Kocisky, Edward Grefenstette, Lasse Espeholt, Will
Kay, Mustafa Suleyman, and Phil Blunsom. 2015.
Teaching machines to read and comprehend. In Ad-
vances in Neural Information Processing Systems,
pages 1684–1692.
[Jean et al.2014] S´ebastien Jean, Kyunghyun Cho,
Roland Memisevic, and Yoshua Bengio. 2014. On
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