Modeling Coverage for Neural Machine Translation

0.76 MB·

by  Zhaopeng Tu

Modeling Coverage for Neural Machine Translation
ZhaopengTu† ZhengdongLu† YangLiu‡ XiaohuaLiu† HangLi†
†Noah’sArkLab,HuaweiTechnologies,HongKong
{tu.zhaopeng,lu.zhengdong,liuxiaohua3,hangli.hl}@huawei.com
‡DepartmentofComputerScienceandTechnology,TsinghuaUniversity,Beijing
[email protected]
Abstract ture long-distance reordering, which is a signifi-
cantchallengeinSMT.
Attention mechanism has enhanced state-
NMT has a serious problem, however, namely
6 of-the-art Neural Machine Translation lack of coverage. In phrase-based SMT (Koehn
1 (NMT) by jointly learning to align and
et al., 2003), a decoder maintains a coverage vec-
0
translate. Ittendstoignorepastalignment
2 tortoindicatewhetherasourcewordistranslated
g information, however, which often leads or not. This is important for ensuring that each
to over-translation and under-translation.
u sourcewordistranslatedindecoding. Thedecod-
A To address this problem, we propose
ing process is completed when all source words
coverage-based NMT in this paper. We
6 are “covered” or translated. In NMT, there is no
maintain a coverage vector to keep track such coverage vector and the decoding process
] oftheattentionhistory. Thecoveragevec-
L ends only when the end-of-sentence mark is pro-
torisfedtotheattentionmodeltohelpad-
C duced. We believe that lacking coverage might
justfutureattention, whichletsNMTsys-
. result in the following problems in conventional
s
c tem to consider more about untranslated NMT:
[ source words. Experiments show that

6 the proposed approach significantly im- 1. Over-translation: some words are unneces-
v proves both translation quality and align- sarilytranslatedformultipletimes;
1
mentqualityoverstandardattention-based
1
8 NMT.1 2. Under-translation: some words are mistak-
4 enlyuntranslated.
0 1 Introduction
. Specifically,inthestate-of-the-artattention-based
1
0 The past several years have witnessed the rapid NMTmodel(Bahdanauetal.,2015),generatinga
6 progress of end-to-end Neural Machine Transla- target word heavily depends on the relevant parts
1
: tion (NMT) (Sutskever et al., 2014; Bahdanau et of the source sentence, and a source word is in-
v
al., 2015). Unlike conventional Statistical Ma- volved in generation of all target words. As a
i
X chineTranslation(SMT)(Koehnetal.,2003;Chi- result, over-translation and under-translation in-
r ang, 2007), NMT uses a single and large neural evitably happen because of ignoring the “cover-
a
networktomodeltheentiretranslationprocess. It age” of source words (i.e., number of times a
enjoys the following advantages. First, the use of source word is translated to a target word). Fig-
distributed representations of words can alleviate ure 1(a) shows an example: the Chinese word
the curse of dimensionality (Bengio et al., 2003). “gua¯nb`ı” is over translated to “close(d)” twice,
Second, there is no need to explicitly design fea- while “be`ipo`” (means “be forced to”) is mistak-
tures to capture translation regularities, which is enlyuntranslated.
quitedifficultinSMT.Instead,NMTiscapableof Inthiswork,weproposeacoveragemechanism
learningrepresentationsdirectlyfromthetraining toNMT(NMT-COVERAGE)toalleviatetheover-
data. Third, Long Short-Term Memory (Hochre- translation and under-translation problems. Basi-
iterandSchmidhuber,1997)enablesNMTtocap- cally, we append a coverage vector to the inter-
mediaterepresentationsofanNMTmodel,which
1Ourcodeispubliclyavailableathttps://github.
com/tuzhaopeng/NMT-Coverage. are sequentially updated after each attentive read
(a) Over-translation and under-translation (b) Coverage model alleviates the problems of
generatedbyNMT. over-translationandunder-translation.
Figure 1: Example translations of (a) NMT without coverage, and (b) NMT with coverage. In conven-
tional NMT without coverage, the Chinese word “gua¯nb`ı” is over translated to “close(d)” twice, while
“be`ipo`”(means“beforcedto”)ismistakenlyuntranslated. Coveragemodelalleviatestheseproblemsby
trackingthe“coverage”ofsourcewords.
during the decoding process, to keep track of the
attention history. The coverage vector, when en-
teringintoattentionmodel,canhelpadjustthefu-
ture attention and significantly improve the over-
all alignment between the source and target sen-
tences. Thisdesigncontainsmanyparticularcases
forcoveragemodelingwithcontrastingcharacter-
istics, which all share a clear linguistic intuition
andyetcanbetrainedinadatadrivenfashion. No-
tably,weachievesignificantimprovementevenby
simplyusingthesumofpreviousalignmentprob- Figure 2: Architecture of attention-based NMT.
abilities as coverage for each word, as a success- Wheneverpossible,weomitthesourceindexj to
fulexampleofincorporatinglinguisticknowledge maketheillustrationlesscluttered.
intoneuralnetworkbasedNLPmodels.
Experiments show that NMT-COVERAGE sig-
produces the translation by generating one target
nificantly outperforms conventional attention-
wordy ateachtimestep. Givenaninputsentence
i
based NMT on both translation and alignment
x = {x ,...,x }andpreviouslygeneratedwords
1 J
tasks. Figure 1(b) shows an example, in which
{y ,...,y }, the probability of generating next
1 i−1
NMT-COVERAGE alleviates the over-translation
wordy is
i
andunder-translationproblemsthatNMTwithout
(cid:0) (cid:1)
coveragesuffersfrom. P(yi|y<i,x) = softmax g(yi−1,ti,si) (1)
wheregisanon-linearfunction,andt isadecod-
2 Background i
ingstatefortimestepi,computedby
Our work is built on attention-based NMT (Bah-
t = f(t ,y ,s ) (2)
danau et al., 2015), which simultaneously con- i i−1 i−1 i
ducts dynamic alignment and generation of the Here the activation function f(·) is a Gated Re-
target sentence, as illustrated in Figure 2. It current Unit (GRU) (Cho et al., 2014b), and s is
i
a distinct source representation for time i, calcu-
latedasaweightedsumofthesourceannotations:
J
(cid:88)
s = α ·h (3)
i i,j j
j=1
→− ←− (cid:62)
where h = [h(cid:62); h(cid:62)] is the annotation of
j j j
x from a bi-directional Recurrent Neural Net-
j
work(RNN)(SchusterandPaliwal,1997),andits Figure3: Architectureofcoverage-basedattention
weightαi,j iscomputedby model. A coverage vector Ci−1 is maintained to
keeptrackofwhichsourcewordshavebeentrans-
α = exp(ei,j) (4) lated before time i. Alignment decisions αi are
i,j (cid:80)J exp(e ) made jointly taking into account past alignment
k=1 i,k
information embedded in C , which lets the at-
i−1
and
tentionmodeltoconsidermoreaboutuntranslated
sourcewords.
e = a(t ,h )
i,j i−1 j
= v(cid:62)tanh(W t +U h ) (5)
a a i−1 a j
coverageisessentialforSMTsinceitavoidsgaps
is an attention model that scores how well y and
i andoverlapsintranslationofsourcewords.
h match. With the attention model, it avoids the
j
Modeling coverage is also important for
need to represent the entire source sentence with
attention-based NMT models, since they gener-
a single vector. Instead, the decoder selects parts
ally lack a mechanism to indicate whether a cer-
of the source sentence to pay attention to, thus
tain source word has been translated, and there-
exploits an expected annotation s over possible
i
fore are prone to the “coverage” mistakes: some
alignmentsα foreachtimestepi.
i,j
partsofsourcesentencehavebeentranslatedmore
However, the attention model fails to take ad-
thanonceornottranslated. ForNMTmodels, di-
vantage of past alignment information, which is
rectly modeling coverage is less straightforward,
found useful to avoid over-translation and under-
but the problem can be significantly alleviated by
translationproblemsinconventionalSMT(Koehn
keepingtrackoftheattentionsignalduringthede-
et al., 2003). For example, if a source word is
coding process. The most natural way for doing
translated in the past, it is less likely to be trans-
that would be to append a coverage vector to the
lated again and should be assigned a lower align-
annotation of each source word (i.e., h ), which
mentprobability. j
isinitializedasazerovectorbutupdatedafterev-
3 CoverageModelforNMT eryattentivereadofthecorrespondingannotation.
The coverage vector is fed to the attention model
InSMT,acoveragesetismaintainedtokeeptrack
to help adjust future attention, which lets NMT
ofwhichsourcewordshavebeentranslated(“cov-
systemtoconsidermoreaboutuntranslatedsource
ered”)inthepast. Letustakex = {x ,x ,x ,x }
1 2 3 4 words,asillustratedinFigure3.
as an example of input sentence. The initial cov-
erage set is C = {0,0,0,0} which denotes that
3.1 CoverageModel
no source word is yet translated. When a trans-
lation rule bp = (x x ,y y ) is applied, we Since the coverage vector summarizes the atten-
2 3 m m+1
produce one hypothesis labelled with coverage tionrecordforh (andthereforeforasmallneigh-
j
C = {0,1,1,0}. Itmeansthatthesecondandthird bor centering at the jth source word), it will
source words are translated. The goal is to gener- discourage further attention to it if it has been
atetranslationwithfullcoverageC = {1,1,1,1}. heavily attended, and implicitly push the atten-
A source word is translated when it is covered by tion to the less attended segments of the source
one translation rule, and it is not allowed to be sentence since the attention weights are normal-
translatedagaininthefuture(i.e.,hardcoverage). izedtoone. Thiscanpotentiallysolvebothcover-
In this way, each source word is guaranteed to be agemistakesmentionedabove,whenmodeledand
translatedandonlybetranslatedonce. Asshown, learnedproperly.
Formally,thecoveragemodelisgivenby translated yet. We iteratively construct linguis-
tic coverages through accumulation of alignment
(cid:0) (cid:1)
Ci,j = gupdate Ci−1,j,αi,j,Φ(hj),Ψ (6) probabilities generated by the attention model,
eachofwhichisnormalizedbyadistinctcontext-
where
dependent weight. The coverage of source word
x attimestepiiscomputedby
• g (·)isthefunctionthatupdatesC af- j
update i,j
terthenewattentionα attimestepiinthe
i,j i
decodingprocess; 1 1 (cid:88)
C = C + α = α (7)
i,j i−1,j i,j k,j
Φ Φ
j j
• C is a d-dimensional coverage vector sum- k=1
i,j
marizingthehistoryofattentiontilltimestep where Φ is a pre-defined weight which indicates
j
ionhj; thenumberoftargetwordsxj isexpectedtogener-
ate. ThesimplestwayistofollowXuetal.(2015)
• Φ(h )isaword-specificfeaturewithitsown
j inimage-to-captiontranslationtofixΦ = 1forall
parameters;
source words, which means that we directly use
• Ψ are auxiliary inputs exploited in different the sum of previous alignment probabilities with-
sortsofcoveragemodels. out normalization as coverage for each word, as
donein(Cohnetal.,2016).
Equation 6 gives a rather general model, which However,inmachinetranslation,differenttypes
could take different function forms for gupdate(·) of source words may contribute differently to the
and Φ(·), and different auxiliary inputs Ψ (e.g., generation of target sentence. Let us take the
previous decoding state ti−1). In the rest of this sentence pairs in Figure 1 as an example. The
section, we will give a number of representative nouninthesourcesentence“j¯ıchaˇng”istranslated
implementations of the coverage model, which into one target word “airports”, while the adjec-
either leverage more linguistic information (Sec- tive “be`ipo`” is translated into three words “were
tion3.1.1)orresorttotheflexibilityofneuralnet- forced to”. Therefore, we need to assign a dis-
workapproximation(Section3.1.2). tinct Φ for each source word. Ideally, we expect
j
Φ = (cid:80)I α with I being the total number
3.1.1 LinguisticCoverageModel j i=1 i,j
of time steps in decoding. However, such desired
We first consider at linguistically inspired model
value is not available before decoding, thus is not
which has a small number of parameters, as well
suitableinthisscenario.
as clear interpretation. While the linguistically-
inspired coverage in NMT is similar to that in Fertility To predict Φ , we introduce the con-
j
SMT,thereisonekeydifference: itindicateswhat ceptoffertility,whichisfirstlyproposedinword-
percentage of source words have been translated levelSMT(Brownetal.,1993). Fertilityofsource
(i.e., soft coverage). In NMT, each target word yi wordxj tellshowmanytargetwordsxj produces.
is generated from all source words with probabil- In SMT, the fertility is a random variable Φ ,
j
ity αi,j for source word xj. In other words, the whose distribution p(Φj = φ) is determined by
source word xj is involved in generating all tar- the parameters of word alignment models (e.g.,
get words and the probability of generating target IBMmodels). Inthiswork,wesimplifyandadapt
word yi at time step i is αi,j. Note that unlike fertility from the original model and compute the
in SMT in which each source word is fully trans- fertilityΦ by2
j
lated at one decoding step, the source word x is
j
partiallytranslatedateachdecodingstepinNMT. Φ = N(x |x) = N ·σ(U h ) (8)
j j f j
Therefore, the coverage at time step i denotes the
translated ratio of that each source word is trans- where N ∈ R is a predefined constant to denote
lated. the maximum number of target words one source
We use a scalar (d = 1) to represent linguis-
2FertilityinSMTisarandomvariablewithasetoffer-
tic coverage for each source word and employ tility probabilities, n(Φ |x ) = p(Φ ,x), which depends
j j <j
an accumulate operation for g . The initial on the fertilities of previous source words. To simplify the
update
calculation and adapt it to the attention model in NMT, we
value of linguistic coverage is zero, which de-
define the fertility in NMT as a constant number, which is
notes that the corresponding source word is not independentofpreviousfertilities.
dependencies. In this work, we adopt GRU for
the gating activation since it is simple yet power-
ful(Chungetal.,2014). Pleasereferto(Choetal.,
2014b)formoredetailsaboutGRU.
Discussion Intuitively, the two types of models
summarizecoverageinformationin“differentlan-
guages”. Linguistic models summarize coverage
informationinhumanlanguage,whichhasaclear
interpretation to humans. Neural models encode
Figure4: NN-basedcoveragemodel. coverageinformationin“neurallanguage”,which
can be “understood” by neural networks and let
them to decide how to make use of the encoded
wordcanproduce,σ(·)isalogisticsigmoidfunc-
coverageinformation.
tion, and U ∈ R1×2n is the weight matrix. Here
f
we use h to denote (x |x) since h contains in-
j j j 3.2 IntegratingCoverageintoNMT
formation about the whole input sentence with a
strong focus on the parts surrounding x (Bah- Although attention based model has the capabil-
j
danau et al., 2015). Since Φ does not depend on ity of jointly making alignment and translation, it
j
i,wecanpre-computeitbeforedecodingtomini- does not take into consideration translation his-
mizethecomputationalcost. tory. Specifically, a source word that has sig-
nificantly contributed to the generation of target
3.1.2 NeuralNetworkBasedCoverageModel
wordsinthepast,shouldbeassignedloweralign-
We next consider Neural Network (NN) based ment probabilities, which may not be the case in
coveragemodel. WhenCi,j isavector(d > 1)and attentionbasedNMT.Toaddressthisproblem,we
gupdate(·) is a neural network, we actually have proposetocalculatethealignmentprobabilitiesby
anRNNmodelforcoverage, asillustratedinFig- incorporating past alignment information embed-
ure4. Inthiswork,wetakethefollowingform: dedinthecoveragemodel.
Intuitively, at each time step i in the decoding
C = f(C ,α ,h ,t )
i,j i−1,j i,j j i−1
phase, coverage from time step (i − 1) serves as
where f(·) is a nonlinear activation function and an additional input to the attention model, which
t istheauxiliaryinputthatencodespasttrans- provides complementary information of that how
i−1
lation information. Note that we leave out the likely the source words are translated in the past.
word-specific feature function Φ(·) and only take We expect the coverage information would guide
the input annotation h as the input to the cov- theattentionmodeltofocusmoreonuntranslated
j
erage RNN. It is important to emphasize that the source words (i.e., assign higher alignment prob-
NN-based coverage model is able to be fed with abilities). In practice, we find that the coverage
arbitrary inputs, such as the previous attentional model does fulfill the expectation (see Section 5).
contexts . HereweonlyemployC forpast The translated ratios of source words from lin-
i−1 i−1,j
alignmentinformation,t forpasttranslationin- guistic coverages negatively correlate to the cor-
i−1
formation,andh forword-specificbias.3 respondingalignmentprobabilities.
j
More formally, we rewrite the attention model
Gating The neural function f(·) can be either a
inEquation5as
simple activation function tanh or a gating func-
tion that proves useful to capture long-distance
e = a(t ,h ,C )
i,j i−1 j i−1,j
3Inourpreliminaryexperiments,consideringmoreinputs
(e.g., current and previous attentional contexts, unnormal- = va(cid:62)tanh(Wati−1+Uahj +VaCi−1,j)
ized attention weights e ) does not always lead to better
i,j
translation quality. Possible reasons include: 1) the inputs
containsduplicateinformation,and2)moreinputsintroduce whereCi−1,j isthecoverageofsourcewordxj be-
moreback-propagationpathsandthereforemakeitdifficult fore time i. V ∈ Rn×d is the weight matrix for
a
to train. In our experience, one principle is to only feed
coverage with n and d being the numbers of hid-
thecoveragemodelinputsthatcontaindistinctinformation,
whicharecomplementarytoeachother. denunitsandcoverageunits,respectively.
4 Training of alignment probabilities and the expected fertil-
itydoesnotholdinthisscenario.
We take end-to-end learning for the NMT-
COVERAGE model, which learns not only the pa- 5 Experiments
rametersforthe“original”NMT(i.e.,θforencod-
5.1 Setup
ing RNN, decoding RNN, and attention model)
but also the parameters for coverage modeling We carry out experiments on a Chinese-English
(i.e., η for annotation and guidance of attention) . translation task. Our training data for the trans-
Morespecifically,wechoosetomaximizethelike- lation task consists of 1.25M sentence pairs ex-
lihood of reference sentences as most other NMT tracted from LDC corpora4 , with 27.9M Chinese
models(see,however(Shenetal.,2016)): wordsand34.5MEnglishwordsrespectively. We
chooseNIST2002datasetasourdevelopmentset,
N
(θ∗,η∗) = argmax(cid:88)logP(y |x ;θ,η) (9) andtheNIST2005,2006and2008datasetsasour
n n
θ,η test sets. We carry out experiments of the align-
n=1
menttaskontheevaluationdatasetfrom(Liuand
No auxiliary objective For the coverage model
Sun, 2015), which contains 900 manually aligned
with a clearer linguistic interpretation (Section
Chinese-English sentence pairs. We use the case-
3.1.1), it is possible to inject an auxiliary objec-
insensitive4-gramNISTBLEUscore(Papineniet
tivefunctiononsomeintermediaterepresentation.
al., 2002) for the translation task, and the align-
More specifically, we may have the following ob-
ment error rate (AER) (Och and Ney, 2003) for
jective:
the alignment task. To better estimate the qual-
N (cid:40) ity of the soft alignment probabilities generated
(cid:88)
(θ∗,η∗) =argmax logP(y |x ;θ,η) by NMT, we propose a variant of AER, naming
n n
θ,η n=1 SAER:
J I (cid:41)
−λ(cid:110)(cid:88)(Φ −(cid:88)α )2;η(cid:111) SAER = 1− |MA×MS|+|MA×MP|
j i,j
|M |+|M |
A S
j=1 i=1
where A is a candidate alignment, and S and P
wheretheterm(cid:8)(cid:80)J (Φ −(cid:80)I α )2;η(cid:9)pe-
j=1 j i=1 i,j are the sets of sure and possible links in the ref-
nalizesthediscrepancybetweenthesumofalign-
erence alignment respectively (S ⊆ P). M de-
ment probabilities and the expected fertility for
notesalignmentmatrix,andforbothM andM
S P
linguistic coverage. This is similar to the more
we assign the elements that correspond to the ex-
explicittrainingforfertilityasinXuetal.(2015),
isting links in S and P with probabilities 1 while
whichencouragesthemodeltopayequalattention
assign the other elements with probabilities 0. In
toeverypartoftheimage(i.e.,Φ = 1). However,
j thisway,weareabletobetterevaluatethequality
our empirical study shows that the combined ob-
ofthesoftalignmentsproducedbyattention-based
jectiveconsistentlyworsensthetranslationquality
NMT. We use sign-test (Collins et al., 2005) for
whileslightlyimprovesthealignmentquality.
statisticalsignificancetest.
Our training strategy poses less constraints on
Forefficienttrainingoftheneuralnetworks,we
thedependencybetweenΦ andtheattentionthan
j limitthesourceandtargetvocabulariestothemost
amoreexplicitstrategytakenin(Xuetal.,2015).
frequent 30K words in Chinese and English, cov-
We let the objective associated with the transla-
ering approximately 97.7% and 99.3% of the two
tionquality(i.e.,thelikelihood)todrivethetrain-
corpora respectively. All the out-of-vocabulary
ing, as in Equation 9. This strategy is arguably
words are mapped to a special token UNK. We set
advantageous,sincetheattentionweightonahid-
N = 2forthefertilitymodelinthelinguisticcov-
den state h cannot be interpreted as the propor-
j erages. We train each model with the sentences
tionofthecorrespondingwordbeingtranslatedin
of length up to 80 words in the training data. The
thetargetsentence. Foronething,thehiddenstate
wordembeddingdimensionis620andthesizeof
h , after the transformation from encoding RNN,
j a hidden layer is 1000. All the other settings are
bears the contextual information from other parts
thesameasin(Bahdanauetal.,2015).
ofthesourcesentence,andthuslosestherigidcor-
4The corpora include LDC2002E18, LDC2003E07,
respondencewiththecorrespondingword. There-
LDC2003E14, Hansards portion of LDC2004T07,
fore, penalizing the discrepancy between the sum LDC2004T08andLDC2005T06.
# System #Params MT05 MT06 MT08 Avg.
1 Moses – 31.37 30.85 23.01 28.41
2 GroundHog 84.3M 30.61 31.12 23.23 28.32
3 +Linguisticcoveragew/ofertility +1K 31.26† 32.16†‡ 24.84†‡ 29.42
4 +Linguisticcoveragew/fertility +3K 32.36†‡ 32.31†‡ 24.91†‡ 29.86
5 +NN-basedcoveragew/ogating(d = 1) +4K 31.94†‡ 32.11†‡ 23.31 29.12
6 +NN-basedcoveragew/gating(d = 1) +10K 31.94†‡ 32.16†‡ 24.67†‡ 29.59
7 +NN-basedcoveragew/gating(d = 10) +100K 32.73†‡ 32.47†‡ 25.23†‡ 30.14
Table1: Evaluationoftranslationquality. ddenotesthedimensionofNN-basedcoverages,and†and‡
indicate statistically significant difference (p < 0.01) from GroundHog and Moses, respectively. “+” is
ontopofthebaselinesystemGroundHog.
We compare our method with two state-of-the- Linguistic Coverages (Rows 3 and 4): Two
artmodelsofSMTandNMT5: observations can be made. First, the simplest
linguistic coverage (Row 3) already significantly
• Moses (Koehn et al., 2007): an open source
improves translation performance by 1.1 BLEU
phrase-based translation system with default
points, indicating that coverage information is
configuration and a 4-gram language model
veryimportanttotheattentionmodel. Second,in-
trainedonthetargetportionoftrainingdata.
corporatingfertilitymodelbooststheperformance
• GroundHog (Bahdanau et al., 2015): an by better estimating the covered ratios of source
attention-basedNMTsystem. words.
5.2 TranslationQuality
NN-based Coverages (Rows 5-7): (1) Gating
Table 1 shows the translation performances mea- (Rows5and6): BothvariantsofNN-basedcover-
suredinBLEUscore. ClearlytheproposedNMT- ages outperform GroundHog with averaged gains
COVERAGE significantly improves the translation of 0.8 and 1.3 BLEU points, respectively. In-
qualityinallcases,althoughtherearestillconsid- troducing gating activation function improves the
erabledifferencesamongdifferentvariants. performanceofcoveragemodels,whichisconsis-
tent with the results in other tasks (Chung et al.,
Parameters Coveragemodelintroducesfewpa-
2014). (2) Coverage dimensions (Rows 6 and 7):
rameters. The baseline model (i.e., GroundHog)
Increasing the dimension of coverage models fur-
has 84.3M parameters. The linguistic coverage
ther improves the translation performance by 0.6
using fertility introduces 3K parameters (2K for
point in BLEU score, at the cost of introducing
fertility model), and the NN-based coverage with
moreparameters(e.g.,from10Kto100K).6
gating introduces 10K×d parameters (6K×d for
gating), where d is the dimension of the coverage
Subjective Evaluation We also conduct a sub-
vector. In this work, the most complex coverage
jective evaluation to validate the benefit of in-
model only introduces 0.1M additional parame-
corporating coverage. Two human evaluators are
ters,whichisquitesmallcomparedtothenumber
asked to evaluate the translations of 200 source
ofparametersintheexistingmodel(i.e.,84.3M).
sentences randomly sampled from the test sets
Speed Introducing the coverage model slows withoutknowingfromwhich systematranslation
down the training speed, but not significantly. is selected. Table 2 shows the results of subjec-
WhenrunningonasingleGPUdeviceTeslaK80, tive evaluation on translation adequacy and flu-
thespeedofthebaselinemodelis960targetwords ency.7 GroudHoghasalowadequacysince25.0%
persecond. System4(“+Linguisticcoveragewith of the source words are under-translated. This is
fertility”) has a speed of 870 words per second,
6Inapilotstudy, furtherincreasingthecoveragedimen-
while System 7 (“+NN-based coverage (d=10)”) siononlyslightlyimprovedthetranslationperformance.One
achievesaspeedof800wordspersecond. possiblereasonisthatencodingtherelativelysimplecover-
ageinformationdoesnotrequiretoomanydimensions.
5Therearerecentprogressonaggregatingmultiplemod- 7Fluencymeasureswhetherthetranslationisfluent,while
elsorenlargingthevocabulary(e.g., in (Jeanetal., 2015)), adequacymeasureswhetherthetranslationisfaithfultothe
butherewefocusonthegenericmodels. originalsentence(Snoveretal.,2009).
Under- Over-
Model Adequacy Fluency
Translation Translation
GroundHog 3.06 3.54 25.0% 4.5%
+NNcov. w/gating(d=10) 3.28 3.73 16.7% 2.7%
Table2: Subjectiveevaluationoftranslationadequacyandfluency. Thenumbersinthelasttwocolumns
denotethepercentagesofsourcewordsareunder-translatedandover-translated,respectively.
(a)Groundhog (b)+NNcov. w/gating(d=10)
Figure 5: Example alignments. Using coverage mechanism, translated source words are less likely to
contributetogenerationofthetargetwordsnext(e.g.,top-rightcornerforthefirstfourChinesewords.).
System SAER AER tionsummarizingattentionhistoryoneachsource
GroundHog 67.00 54.67 word. More specifically, linguistic coverage with
+Ling. cov. w/ofertility 66.75 53.55 fertilitysignificantlyreducesalignmenterrorsun-
+Ling. cov. w/fertility 64.85 52.13 derbothmetrics,inwhichfertilityplaysanimpor-
+NNcov. w/ogating(d=1) 67.10 54.46 tantrole. NN-basedcoverages, however, doesnot
+NNcov. w/gating(d=1) 66.30 53.51 significantlyreducealignmenterrorsuntilincreas-
+NNcov. w/gating(d=10) 64.25 50.50 ing the coverage dimension from 1 to 10. It in-
dicates that NN-based models need slightly more
Table 3: Evaluation of alignment quality. The
dimensionstoencodethecoverageinformation.
lowerthescore,thebetterthealignmentquality.
Figure 5 shows an example. The coverage
mechanism does meet the expectation: the align-
ments are more concentrated and most impor-
mainly due to the serious under-translation prob-
tantly, translated source words are less likely to
lems on long sentences that consist of several
getinvolvedingenerationofthetargetwordsnext.
sub-sentences, in which some sub-sentences are
For example, the first four Chinese words are as-
completely ignored. Incorporating coverage sig-
signed lower alignment probabilities (i.e., darker
nificantly alleviates these problems, and reduces
color) after the corresponding translation “roma-
33.2% and 40.0% of under-translation and over-
niareinforcesoldbuildings”isproduced.
translation errors respectively. Benefiting from
this,coveragemodelimprovesbothtranslationad-
equacyandfluencybyaround0.2points. 5.4 EffectsonLongSentences
Following Bahdanau et al. (2015), we group sen-
5.3 AlignmentQuality
tences of similar lengths together and compute
Table 3 lists the alignment performances. We BLEU score and averaged length of translation
find that coverage information improves atten- for each group, as shown in Figure 6. Cho et
tionmodelasexpectedbymaintaininganannota- al.(2014a)showthattheperformanceofGround-
Figure 6: Performance of the generated translations with respect to the lengths of the input sentences.
Coveragemodelsalleviateunder-translationbyproducinglongertranslationsonlongsentences.
hog drops rapidly when the length of input sen- jordan ’s average score points to UNK
tence increases. Our results confirm these find- this year . he received surgery before
ings. One main reason is that Groundhog pro- three weeks ,withateamintheperiod
duces much shorter translations on longer sen- of4to8.
tences (e.g., > 40, see right panel in Figure 6),
and thus faces a serious under-translation prob- inwhichtheunder-translationisrectified.
lem. NMT-COVERAGEalleviatesthisproblemby The quantitative and qualitative results show
incorporatingcoverageinformationintotheatten- that the coverage models indeed help to allevi-
tion model, which in general pushes the attention ateunder-translation,especiallyforlongsentences
to untranslated parts of the source sentence and consistingofseveralsub-sentences.
implicitly discourages early stop of decoding. It
is worthy to emphasize that both NN-based cov- 6 RelatedWork
erages(withgating, d = 10)andlinguisticcover-
Our work is inspired by recent works on im-
ages (with fertility) achieve similar performances
provingattention-basedNMTwithtechniquesthat
onlongsentences,reconfirmingourclaimthatthe
have been successfully applied to SMT. Follow-
two variants improve the attention model in their
ingthesuccessofMinimumRiskTraining(MRT)
ownways.
in SMT (Och, 2003), Shen et al. (2016) proposed
Asanexample,considerthissourcesentencein
MRT for end-to-end NMT to optimize model pa-
thetestset:
rameters directly with respect to evaluation met-
qia´oda¯nbeˇnsa`ij`ıp´ıngju¯nde´fe¯n24.3fe¯n
rics. Based on the observation that attention-
, ta¯ za`i sa¯n zho¯u qia´n jie¯sho`u shoˇushu`
basedNMTonlycapturespartialaspectsofatten-
,qiu´du`ıza`icˇıq¯ıjia¯n4she`ng8fu` .
tional regularities, Cheng et al. (2016) proposed
Groundhogtranslatesthissentenceinto: agreement-based learning (Liang et al., 2006) to
encourage bidirectional attention models to agree
jordan achieved an average score of
on parameterized alignment matrices. Along the
eightweeksaheadwithasurgicaloper-
same direction, inspired by the coverage mecha-
ationthreeweeksago.
nism in SMT, we propose a coverage-based ap-
in which the sub-sentence “, qiu´du`ı za`i cˇı q¯ıjia¯n proach to NMT to alleviate the over-translation
4 she`ng 8 fu`” is under-translated. With the (NN- andunder-translationproblems.
based) coverage mechanism, NMT-COVERAGE Independent from our work, Cohn et al. (2016)
translatesitinto: andFengetal.(2016)madeuseoftheconceptof
“fertility” for the attention model, which is sim- [Brownetal.1993] Peter E. Brown, Stephen A. Della
ilar in spirit to our method for building the lin- Pietra, Vincent J. Della Pietra, and Robert L. Mer-
cer. 1993. The mathematics of statistical machine
guistically inspired coverage with fertility. Cohn
translation: Parameter estimation. Computational
et al. (2016) introduced a feature-based fertility
Linguistics,19(2):263–311.
thatincludesthetotalalignmentscoresforthesur-
[Chengetal.2016] YongCheng, ShiqiShen, Zhongjun
roundingsourcewords. Incontrast,wemakepre-
He,WeiHe,HuaWu,MaosongSun,andYangLiu.
diction of fertility before decoding, which works
2016. Agreement-basedJointTrainingforBidirec-
asanormalizertobetterestimatethecoveragera- tionalAttention-basedNeuralMachineTranslation.
tio of each source word. Feng et al. (2016) used InIJCAI2016.
the previous attentional context to represent im-
[Chiang2007] David Chiang. 2007. Hierarchical
plicitfertilityandpassedittotheattentionmodel,
phrase-basedtranslation. CL.
which is in essence similar to the input-feed
[Choetal.2014a] Kyunghyun Cho, Bart van Merrien-
methodproposedin(Luongetal.,2015). Compar-
boer, Dzmitry Bahdanau, and Yoshua Bengio.
atively,wepredictexplicitfertilityforeachsource
2014a. On the properties of neural machine trans-
wordbasedonitsencodingannotation,andincor- lation: encoder–decoderapproaches. InSSST2014.
porate it into the linguistic-inspired coverage for
[Choetal.2014b] Kyunghyun Cho, Bart van Merrien-
attentionmodel.
boer, Caglar Gulcehre, Fethi Bougares, Holger
Schwenk, and Yoshua Bengio. 2014b. Learning
7 Conclusion
phrase representations using rnn encoder-decoder
forstatisticalmachinetranslation. InEMNLP2014.
We have presented an approach for enhancing
NMT, which maintains and utilizes a coverage [Chungetal.2014] Junyoung Chung, Caglar Gulcehre,
vector to indicate whether each source word is KyungHyun Cho, and Yoshua Bengio. 2014. Em-
piricalevaluationofgatedrecurrentneuralnetworks
translatedornot. ByencouragingNMTtopayless
onsequencemodeling. arXiv.
attentiontotranslatedwordsandmoreattentionto
untranslatedwords,ourapproachalleviatesthese- [Cohnetal.2016] Trevor Cohn, Cong Duy Vu Hoang,
rious over-translation and under-translation prob- Ekaterina Vylomova, Kaisheng Yao, Chris Dyer,
and Gholamreza Haffari. 2016. Incorporating
lems that traditional attention-based NMT suffers
Structural Alignment Biases into an Attentional
from. We propose two variants of coverage mod-
NeuralTranslationModel. InNAACL2016.
els: linguistic coverage that leverages more lin-
[Collinsetal.2005] Michael Collins, Philipp Koehn,
guistic information and NN-based coverage that
andIvonaKucˇerova´. 2005. Clauserestructuringfor
resortstotheflexibilityofneuralnetworkapprox-
statisticalmachinetranslation. InACL2005.
imation . Experimental results show that both
variantsachievesignificantimprovementsinterms [Fengetal.2016] Shi Feng, Shujie Liu, Mu Li, and
Ming Zhou. 2016. Implicit distortion and fertil-
of translation quality and alignment quality over
itymodelsforattention-basedencoder-decodernmt
NMTwithoutcoverage.
model. arXiv.
Acknowledgement [HochreiterandSchmidhuber1997] Sepp Hochreiter
and Ju¨rgen Schmidhuber. 1997. Long short-term
This work is supported by China National 973 memory. NeuralComputation.
project 2014CB340301. Yang Liu is supported
[Jeanetal.2015] Se´bastien Jean, Kyunghyun Cho,
by the National Natural Science Foundation of
RolandMemisevic, andYoshuaBengio. 2015. On
China (No. 61522204) and the 863 Program
using very large target vocabulary for neural ma-
(2015AA011808). We thank the anonymous re- chinetranslation. InACL2015.
viewersfortheirinsightfulcomments.
[Koehnetal.2003] Philipp Koehn, Franz Josef Och,
and Daniel Marcu. 2003. Statistical phrase-based
translation. InNAACL2003.
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