Table Of ContentBidirectional American Sign Language to English Translator
Hardie Cate Zeshan Hussain
[email protected] [email protected]
December 11, 2015
1 Introduction
phasis in the literature on the conversion between the
grammars of the languages, which is what we examine in
7 IntheUSalone,thereareapproximately900,000hearing- this project.
1 impaired people whose primary mode of conversation is
0 sign language. For these people, communication with
2 3 Task Definition
non-signers is a daily struggle, and they are often dis-
n advantaged when it comes to finding a job, accessing
a health care, etc. There are a few emerging technolo- Let us formalize the notation for this task. For the sake
J of simplicity, we will use ASL as our “source language”
giesdesignedtotranslatesignlanguagetoEnglishinreal
0 and English as our “target language.” In general, S will
time,butmostofthecurrentresearchattemptstoconvert
1 be used to refer to the sentence in ASL, where S is a
raw signs into English words. This aspect of the trans-
sequence of signs S ,...,S and m is the length of the
] lation is certainly necessary, but it does not take into 1 m
L sentence. Each sign S is represented by a word with up-
account the grammatical differences between signed lan- i
C percase letters. We also include special ”gesture tokens,”
guages and spoken languages. In this paper, we outline
s. our bidirectional translation system that converts sen- whichrepresentgesturesorhandmovements(e.g.,[point],
c tences from American Sign Language (ASL) to English, [head shake]) that are not signs in and of themselves but
[ rather modify other signs in the sentence. As a special
and vice versa.
case, which we address later, commas are also treated as
1 To perform machine translation between ASL and En-
v glish, we utilize a generative approach. Specifically, we individual sign tokens. Similarly, we will use E to refer
5 to the English sentence, where E is a sequence of words
employanadjustmenttotheIBMword-alignmentmodel
9 E ,...,E and l is the length of the sentence.
1 (IBM WAM1)[14] where we define language models for 1 l
7
2 English and ASL, as well as a translation model, and at- S :WRISTWATCH [point], WHO GIVE−YOU?
input
0 tempt to generate a translation that maximizes the pos-
1. terior distribution defined by these models. Using these Eoutput :Who gave you that wristwatch?
0 models, we are able to quantify the concepts of fluency
In the backward direction, the input is a grammatically
7 and faithfulness of a translation between languages.
correct English sentence and the output is a sequence of
1
signs represented by words. An example for this input is
:
v
i 2 Related Work E :He fell down.
X input
r Soutput :FELL.
a In examining papers and projects related to this topic,
we have discovered that translation between signed and To evaluate translations generated by our system as well
spoken languages is still largely an open problem. The as the baselines in both directions, we use the BLEU-
few emerging technologies that attempt to tackle this is- 2 score,[10] which takes in a predicted sentence and a
sue are only capable of translating single words or short reference sentence, both in the target language. Let p
n
phrasesofASLintoEnglish. Infact,mostapproachesfo- be the the ratio of the number of shared n-grams to the
cusonanalyzingvideosofsignsandconvertingtheseinto total number of n-grams in the predicted sentence. Then
words, which a number of recent CS229 projects have for a predicted sentence p and reference sentence s, the
done. However, several teams at the University of Penn- BLEU-N score is given by
sylvania have taken a more grammatical approach [15]
N
ttohethoethperrodbilreemctioofnE. nTghliesrhe-thoa-As aSlLsotrbaenesnlaatnionItabluiatnntoetaimn BLEU2(p,r)=e(1−||pr||)(cid:89)pN.
i=1
that has designed a tentative translation system for Ital-
iansignlanguage,[7]butthereareveryfewparallelswith Thus BLEU-2 is essentially the ratio of shared uni-
respect to grammatical structures that we can draw for grams and bigrams between the predicted translation
this project. In short, there has been relatively little em- and the gold-standard translation to the number that
1
appear in the predicted translation. The exponen- 5.1 Baseline and Oracle
tial factor at the beginning of the above expression is
Because of the bidirectional nature of the problem, we
called the brevity penalty. Because the rest of the
have two baselines. For a baseline in English-to-ASL di-
BLEU expression measures the precision of the predic-
rection, we use a unigram cost function over English to
tion, it does not penalize the prediction for leaving out
detect the “most important” words in the sentence and
words. The role of the brevity penalty is to penal-
directly translate each word into ASL, preserving the or-
ize predicted sentences that are shorter than their ref-
derofthesewords. Topickthemostimportantwords,our
erence counterparts. As a concrete example, consider
baselineselectsthehighestcostwordsabovesomethresh-
translating ”YOU LIKE COLOR BROWN?” to the En-
old based on our unigram cost function. In this case, a
glish ”You like brown color?” with the correct reference
higher cost implies a lower frequency in the English lan-
”Do you like the color brown?”. The BLEU-2 score of
this prediction is e1−46(4)(1)=0.202 guage, suggesting higher importance. For our baseline
4 3 intheotherdirection,wedirectlytranslateeachsigninto
The BLEU-2 metric is used primarily to rate transla-
Englishandinsertsmallhelperwords(suchas“a,”“the,”
tionsatacorpuslevel,soitdoesnotperformaswellwhen
“and,” etc.), treating this as a search problem that tries
used to evaluate translations of single sentences. Despite
to minimize the cost of the English sentence using a bi-
that, the BLEU-2 score should be a sufficient evaluation
gram cost function. To test these baselines, we utilized
metric for the purposes of this project, since it is still
the same BLEU score as we did for the results generated
the most common machine translation evaluator and is
by our IBM word alignment system. This approach al-
relatively simple to interpret.
lowed us to easily compare the baseline results and IBM
WAMresults,whicharereportedinthe’Results’section.
Forouroraclesinbothdirections,wehavesomeonefa-
4 Data
miliar with both languages translate each sentence into
the other language. These translations are adequate or-
Our data consists of 579 ASL/English translation pairs
acles because they make the most sense from a human
scraped from a repository from lifeprint.com,[12] a web-
standpoint. Furthermore, the oracles serve as appropri-
sitededicatedtoASLeducation. Eachpairisasingleac-
ate gold standard translations that can be used in the
curatetranslationofanASLsentenceorquestionintoEn-
BLEU score calculation.
glish. ManyoftheASLsentencescontaingesturetokens.
These pairs are not specifically designed to be transla-
tionsfromEnglishtoASLandingeneral, translationbe- 5.2 Custom WAM
tweenlanguagesisnotsymmetric. However,wemakethe
TheIBMWAM1isacommonmachinetranslationmodel
simplifying assumption that this translation is symmet-
thatwecustomizetotakeadvantageofcommonconstruc-
ric primarily because we do not have a thorough enough
tions in ASL and allow us to modify how the translation
understanding of ASL to translate from English to ASL.
modelandlanguagemodelsareconstructed. Thecustom
Totestouralgorithms,wesplitthedataintotwomain
model is meant to give us flexibility in our translations.
sets–atrainingset(80%oforiginaldata)andatestingset
We now describe the general workflow of our model in
(remaining20%). Wealsohaveathirdset, calledthede-
the ASL to English direction. This workflow applies to
velopment set, that we use to tune our hyperparameters.
the other direction as well, except with E switched with
This set is constructed by pulling out 10 examples from
S: 1). For each potential translation, we calculate p(E)
the training set and treating them as our development
2). Then, given this channel input, we calculate p(S|E)
set. A more standard convention is to use a 70%-30%
in the “noisy channel” 3). Finally, we choose the E that
split for training and testing data, but we decided to use
gives the highest posterior probability given these two
alargertrainingsetsizebecauseweusethesesignsasour
probabilities (likelihood and prior). The p(E) is repre-
entire corpus for our ASL language model. We also use
sented by the language model while the p(S|E) is rep-
small subsets of the data to run tests to assess the per-
resented by the translation model. We will derive this
formance of specific aspects of our system. For example,
formulation below.
we construct a test set from the original test set to judge
the performance of ”comma-trigram” feature of our ASL
language model. We construct another test set to handle 5.2.1 Modeling
gesture tokens. To assess these tests, we use the BLEU-2
Forconcreteness,wewillusearunningtranslationexam-
metric described in the task definition.
pleinthedescriptionsofourmodelsandalgorithms. Con-
siderthefollowinggold-standardtranslationfromASLto
5 Technical Approach English:
S :YOU LIKE LEARN SIGN?
Below,wedetailthebaselines,oracles,andtheIBMword input
E :Do you like learning sign language?
alignment method for this problem. output
2
To derive the models that we will use in the translation, the comma and a common word after the comma. The
weconsiderthefollowingoptimizationproblem(notethat secondcaveatisthatweincorporategesturetokens,which
once again we are writing this derivation in the ASL to are contained in brackets, into our corpus. Our language
English direction but the same derivation applies to the model does not, however, treat these gesture tokens any
other direction): differently than regular signs.
Given a sentence S in ASL, we want to find an English
sentence such that:
5.2.3 Translation Model
E =arg max P(E|S)
Now, we wish to estimate the probability P(S|E) given
E∈English
a trained translation model. We represent this model as
E =arg max P(S|E)P(E)
E∈English a mapping from ”sign-English” pairs to probabilities (of
those pairs) using a dictionary in python. Training this
The probability P(E) represents the fluency of E in En-
model requires us to introduce a set A = a ,...,a of
1 J
glish, i.e. how much the translation makes sense to a na-
alignment variables where a represents the index of the
j
tive speaker, while P(S|E) represents the faithfulness of
word in the English sentence that translates to the jth
thetranslationfromS toE,i.e. ifthetranslationreflects
word s in S. We also allow these variables to take on
j
the actual meaning of what is being said. We determine
a value of 0, which represents a “null” word in the En-
P(E) using a language model for English and P(S|E)
glish sentence. This null word allows for the possibility
using a translation model from English to ASL. (Note
thatthereisnowordintheEnglishsentencethatdirectly
that in our overall translation from ASL to English, we
translates to some sign s in S. In the case of our exam-
j
modeltheprobabilityofagiventranslationfromEnglish
ple above, if A specifies that “learning” in E translates
to ASL.)
to “LEARN” in S, then a = 4, since “LEARN” is the
3
thirdwordinS and“learning”isinthefourthpositionof
5.2.2 Language Model E. Thus, Acanbethoughtofasamany-to-onemapping
from words in E to signs in S. (In general, this mapping
To compute P(E) for a given English sentence E, we use
would need to be many-to-many since we sometimes re-
an n-gram cost function. For instance, in our example
quire phrase-to-phrase translation, but for our purposes
sentence E above in the case where n=3,
the many-to-one assumption is reasonable because gen-
erally speaking, multiple English words map to only one
p(E)=p (null,null,”do”)·p (null,”do”,”you”)
3 3
sign.) Then, to compute P(S|E), we marginalize out the
·p (”learning”,”sign”,”language?”)
3 alignment variables
where p3 is a 3-gram cost function. We have imported (cid:88) (cid:88)
P(S,A|E)= P(S|A,E)P(A|E).
n-gram cost functions from http://www.ngrams.info/ [8]
for n=2,3,4,5. A A
There are a couple of caveats with the above approach
Now, let I be the number of words in E. For fixed I,
whentryingtocreatealanguagemodelforASL,whichis
weassumethateachpossiblealignmentforeachlengthis
necessaryfortranslationintheotherdirection(i.e. tocal- equallylikely,whichgivesusthatP(A|E)= (cid:15) . This
culate P(S)). The first is that our ASL language model (1+I)J
probabilitycanbethoughtofasanormalizationfactorfor
is basically a unigram model that has additional com-
the probability, P(S|E). Indeed, P(S|A,E) is given by,
ponents. In addition to each sign having an associated
probability, which is a simple unigram model, we add a
J
comma trigram construction. This construction incorpo- p(S|A,E)= (cid:89)t(s ,e ),
rates into the language model the following grammatical j aj
j=1
structure that is commonly found in ASL,
where t(s,e) is the probability of translating e as s.
([NOUN]|[NOUNPHRASE])[,][PHRASE] Therefore, this gives us the following for P(S|E),
In other words, an infrequent (uncommon) noun will be
J
(cid:88) (cid:15) (cid:89)
followed by a comma, which is followed by a phrase that p(S|E)= t(s ,e ),
generallydescribesorreferstothenoun. Thephraseafter (1+I)J j aj
A j=1
thecommausuallystartswithamorefrequent(common)
word. The way the comma trigram encapsulates this is After training on our dataset, constructing a language
byincreasingtheunigramprobabilityofthesecondword model for each language using the corpa we have pro-
and decreasing the unigram probability of the first word. cessed,andgeneratingaparameterizedtranslationmodel
This effectively favors a big difference between the word using the EM algorithm, we can use our decoder to find
before the comma and the word after the comma, which the optimal translation. Both the EM and decoding al-
inturnfavorsanuncommonwordbeingtranslatedbefore gorithms are described in detail in the next section.
3
5.2.4 Algorithms empty hypothesis. Then we iterate through the priority
queues,andforthetophypothesishinthecurrentqueue,
The two primary algorithms used in our implementation
we generate some number of new hypotheses h ,...,h
areanEMalgorithmtotrainourtranslationmodelwhich 1 k
byaddingasingleEnglishwordasthenextwordinh. We
defines P(S|E) and our decoding algorithm. The decod-
placeeachnewhypothesish(cid:48) inq , wherei isthenumber
ingalgorithmisamodifiedversionofthedecodinginIBM i
of signs in S that have been translated to English words
WAM1, which uses a variant of beam search to find
in h(cid:48). Each hypotheses h is prioritized according to
argmaxP(S|E)P(E).
E |E|
(cid:88)
log p(s |e )+W logp(E)
FirstwedescribetheEMalgorithm. Ourlanguagemodel ej j
j=1
maintains a mapping t from pairs (s,e), where s is a sign
and e is an Englishword, toprobabilities t(s,e)=p(s|e). whereE =[e e ...e ]istheEnglishsentencecontained
1 2 |E|
Given our training data containing a list of m transla- in h and W is the language model weight whose purpose
tion pairs (S,E), we can rewrite our estimate of a single is explained below. The aspect related to BEAM search
transition probability as is that we only consider the first k hypotheses in each
q before moving on to q . Thus, we effectively prune
m i i+1
(cid:88)(cid:88)
p(s|e)= p(S ,A |E ) hypotheses that do not appear among the first k for a
i i i
given number of translated signs.
i=1 Ai
m J Modified decoding algorithm
(cid:88)(cid:88) (cid:89)
= p(A |E ) t(s ,e ) Set q to PQ with empty hyp.
i i j aj 0
i=1 Ai j=1 Set each q1,...,ql to empty PQ
for i=0 to l−1 do
where A is the alignment vector for training example i.
i for j =0 to MAX QUEUE SIZE do
To estimate t(s,e) given the data, we need to find the
hyp = q .pop()
i
maximum likelihood of this probability. However, with
for k =1 to l do
theintroductionoftheselatentvariables,itisnotpossible
newhyp = max over s,e of newhyp(s,e)
tofindthislikelihoodinaclosedform. Therefore, weuse
insert new hyp into appropriate queue
the EM algorithm to estimate this likelihood by comput-
end for
ingtheprobabilityofeachpossiblealignmentgivenadis-
end for
tributionoft(s,e)foreachsandeintheE-stepandthen
end for
adjustingthisdistributionbasedonthesealignmentprob-
Return q .pop()
l
ability estimates in the M-step. The algorithm runs as
where newhyp(s,e) is the new hypothesis formed by
follows:
translating s ∈ S to e and newhyp is the maximum
EM for ASL-to-English translation model
according to the priority measure. Once the algorithm
while Not converged do
reaches the final priority queue, all l signs have been
Set t(s,e) uniform, including t(s,NULL).
translated. This implies that the top hypothesis in the
Set counts of e translating to s as C(s,e)=0∀s,e.
priorityqueueisthehypothesisthattranslatesallsignsin
for (S,E) in training set do
S toEnglish(possiblytotheNULLword)andmaximizes
for sign position i=1,...,l do
p(S|E)p(E), i.e., the hypothesis with English sentence
P(a =j|S,E)= t(si,ej)
i (cid:80)j(cid:48)t(si,ej(cid:48))
C(s |e )+=P(a =j|S,E) E =argmaxp(e|S).
end foirai i e∈ql
end for
5.2.5 Algorithms Commentary
for all s,e do
t(s,e)= C(s,e)
(cid:80)s(cid:48)C(s(cid:48),e) Oneofthemostimportantaspectsofthealgorithmisthat
end for a hypothesis h can generate a new hypothesis h(cid:48) that is
end while placed in the same queue as h if it is generated by trans-
Our decoding algorithm is a modification of the conven- lating a sign already translated in h. This is important
tionaldecodingalgorithmusedinIBMWAM1. Weframe because it allows us to translate a single sign to multiple
the problem of finding the best English translation as a English words. In particular, this allows the translated
type of search problem. Let l be the length of S. The English sentence to be longer than the input sign sen-
algorithm is a variant on BEAM search that maintains tence, which is the case with most English translations.
a list of priority queues q ,q ,...,q of hypotheses. Each Another important note concerns the language model
0 1 l
hypothesisconsistsofalistofEnglishwordseeachwitha weight. If this weight is set to 1, the priority is simply
correspondingsigns fromS. Initially,allpriorityqueues p(E|S), i.e., the value we are trying maximize. How-
e
are empty, with the exception of q , which contains the ever, we find that if we try to maximize this value,
0
4
6 Results
As mentioned in the Data section, we split the dataset
into three separate sets: a training set, a development
set, and a test set. For both directions, we train our
models on a training set, tune the hyperparameters (i.e.
thelanguagemodelweight,thequeuesizek,andthetype
ofn-grammodelweareusingforthelanguagemodel[uni-
gram,bigram,trigram])onadevelopmentset,andfinally
findtheaverageBLEU-2scoreonatestset. Furthermore,
our metric for measuring the accuracy of translation on
the test set or the development set is the average of in-
dividual BLEU-2 scores of the translations. Thus, any
BLEU-2 score that is reported in tables or figures will be
the mean BLEU-2 score.
Figure1: ASL-to-ENG:VaryingLanguageModelWeight
6.1 Hyperparameter Tuning Results
To find the optimal hyperparamaters, we ran several ex-
periments. Theinitialsetofexperimentswereruntofind
theoptimalhyperparametersfortranslationfromASLto
English. First, we found the BLEU-2 score on the devel-
opmentsetbykeepingthequeuesizeconstantandadjust-
ing the language model weight using the bigram English
model. Next, we performed the same experiment except
using the trigram English model. Results of these exper-
iments are reported in tables 2 and 3. We find that the
optimal language model weight is 0.1, the optimal queue
size for ASL to English translation is 20, and the opti-
mal language model type is a trigram model. Using this
combination gives us a BLEU-2 score of 0.276 (see Table
3), which is the highest BLEU-2 score that we obtained
while tuning hyperparameters.
Toprovidesomeintuitiononthechoiceofexperiments,
we reasoned that we could converge on the optimal hy-
perparameter combination using a method similar to co-
Figure 2: ASL-to-ENG: Varying Queue Size
ordinate ascent, where we optimize a single parameter
whilekeepingtheotherparametersconstant. Then,after
finding the optimal value for the parameter, repeat the
process with the other parameters while still maintain-
the language model probabilities ”outweigh” the prob- ing the optimal values for the parameters that have been
abilities of the translation model. In effect, the sign alreadyprocessed. Usingthe(20,0.1,’Trigram’)combina-
sentence is likely to be translated as a set of com- tion,weranoursystemonthetestset;theBLEU-2score,
mon English n-grams unrelated to the original sen- reported in Table 1, is 0.1202.
tence. For instance, when we attempt to translate The subsequent experiments involved finding the op-
YOUR SISTER SINGLE?as”Is your sister single?”,our timal hyperparameters for translation from English to
top result is ”in the first”. However, when we introduce
thislanguagemodelweightwithW =0.3,weproducethe
Table 1: Overall BLEU-2 Scores on Test Sets
translation ”Your sister single?”. Although this transla-
tionmightnotbeascommoninEnglishas”in the first”,
BLEU-2 Score
itcertainlypreservesthemeaningoftheoriginalsentence
ASL-to-English 0.1202
much better.
English-to-ASL 0.1802
Comma Trigram Test 0.1201
Just as with BEAM search, our decoding algorithm
Gestures Test 0.1232
is not guaranteed to converge to the optimal result (in
Baseline ASL-to-English 0.1012
fact, this problem in NP-complete), but in many cases it
Baseline English-to-ASL 0.1139
provides good results.
5
ASL. Using a similar methodology to the one used in After running our tuning experiments on the hyper-
the other translation direction, we ran one set of experi- parameters, we noticed a few interesting trends. First,
ments where the queue size was kept constant while the instances in which we use a trigram model for English
language model weight was adjusted. Because we only tend to produce higher BLEU-2 results than with corre-
used a unigram ASL language model while translating in sponding bigram English models, particularly when the
thisdirection,runningonlythissetofexperimentsissuf- maximum queue size in the decoding algorithm is large.
ficient to find the optimal hyperparameter pair. Results This makes sense because the trigram model inherently
of the experiment are reported in Table 4. We find that captures more information than the bigram model. Fur-
the optimal language model weight is 0.1 and the opti- thermore, we see from the two graphs above that for
mal queue size is 20. Using the (20,0.1) combination and large maximum queue sizes, instances with small lan-
running the system on the test set, we obtain a BLEU-2 guagemodelweightsperformbetter. Wecanexplainthis
score, reported in Table 1, of 0.1802. as follows: For large maximum queue sizes, after the first
few iterations of the decoding algorithm, we will see hy-
potheses with common words. If we do not sufficiently
6.2 Experiments for ASL Constructions
dampen the influence of the language model, we will be-
gin to see hypotheses with high priorities resulting from
Thelasttwosetsofexperimentstesthowoursystemper-
commonngrams,andthesewillsteerthesentencegenera-
forms on translating specific ASL constructions. Firstly,
tionawayfromtheoriginalmeaningofthesignsentence.
we perform an experiment on translating only the com-
Therefore, we conclude that models with high maximum
mon comma construction described above in Modeling,
queuesizesandlowlanguagemodelweightsgenerallyper-
by filtering for these test examples in the test set and
form best.
using the optimal ASL to English hyperparameters when
runningonthefilteredtestset. TheBLEU-2scoreresult
for this test is .1201 (see Table 1). Although it is useful to look at how changing these
Finally,thelastexperimenttestshowoursystemtrans- hyperparameters changes the model, it is perhaps even
latessignsequenceswithgesturetokens. Wecreateatest more important to recognize the shortcoming of our cur-
setforthisexperimentinasimilarfashiontowhatwedid rent model. The first issue is with our corpus, which by
for comma constructions, by filtering the original test set most measures is far too small to perform accurate ma-
to only include sign sequences with gesture tokens. The chinetranslation. Wealsolacklargedatabasescontaining
BLEU-2 score for this test is .1232 (see Table 1). sign language as text which limited our language model
forASL.Unfortunately, ourASLlanguagemodelwasfar
too crude to produce accurate results. We oversimplified
7 Analysis the model in several ways, in particular with our comma
trigramstructureandtreatmentofgesturesasequivalent
toothersigns. BecausewearenotfluentinASL,letalone
As a sanity check for our results, it is important to note
fully understand its underlying structure, we had diffi-
that our translation system outperforms the baseline im-
cultyindesigningitslanguage. Infact,toourknowledge,
plementations in both directions. We also observe a dif-
no one else has designed a text-based language model for
ference in performance between the two translational di-
anysignlanguage. Finally,machinetranslationforsingle
rections. The ASL-to-English direction produces an av-
sentencesisinherentlyadifficulttask,sincethesentences
erage BLEU-2 score of 0.12, while the English-to-ASL
lack context. Despite these many challenges and difficul-
gives a higher score of 0.18. The behavior is consistent
ties,oursystemstilltranslatesmanysentenceseffectively
withourexpectationsgoingintotheprojectsinceingoing
between the two languages in both directions.
fromEnglishtoASL,weessentiallyfilteroutunnecessary
information, whereas the other direction requires gener-
ating information in effect.
Table 2: ASL-to-ENG: Bigrams Table 3: ASL-to-ENG: Trigrams
QueueSize LanguageModelWeight BLEU-2Score QueueSize LanguageModelWeight BLEU-2Score
8(bigram) 0.1 0.2360 8(trigram) 0.1 0.0950
0.2 0.1690 0.2 0.1610
0.3 0.1600 0.3 0.1610
10(bigram) 0.1 0.1990 10(trigram) 0.1 0.1270
0.2 0.1690 0.2 0.1440
0.3 0.0700 0.3 0.1160
20(bigram) 0.1 0.1990 20(trigram) 0.1 0.2760
0.2 0.2190 0.2 0.1690
0.3 0.2080 0.3 0.0505
6
Table 4: ENG-to-ASL Table: Language Model Weights [8] N-grams data. http://www.ngrams.info/. Ac-
cessed: 2015-12-11.
QueueSize LanguageModelWeight BLEU-2Score
20 0.1 0.1790 [9] NaturalLanguageProcessing:MachineTranslation.
0.2 0.1140 http://web.stanford.edu/class/cs224n/
0.3 0.1150
handouts/cs224n-lecture3-MT.pdf. Accessed:
15 0.1 0.1004
0.2 0.1000 2015-12-11.
0.3 0.0680
[10] KishorePapinenietal.“BLEU:amethodforauto-
13 0.1 0.0964
0.2 0.1480 matic evaluation of machine translation”. In: Pro-
0.3 0.0860 ceedings of the 40th annual meeting on association
for computational linguistics. Association for Com-
8 Conclusion & Future Work putational Linguistics. 2002, pp. 311–318.
[11] Becky Sue Parton. “Sign language recognition and
While we would like our translations to be as accurate as translation: A multidisciplined approach from the
possible, it is important to note that it is virtually im- field of artificial intelligence”. In: Journal of deaf
possible, even for an oracle, to come up with a perfect studies and deaf education 11.1 (2006), pp. 94–101.
translation. In any language, words and sentences carry
[12] Sign Language Phrases. http://www.lifeprint.
contextual meaning that might be impossible to express
com/asl101/index/sign-language-phrases.
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