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Dialectal Arabic to English Machine Translation:
Pivoting through Modern Standard Arabic
WaelSalloum and NizarHabash
Center for Computational Learning Systems
Columbia University
{wael,habash}@ccls.columbia.edu
Abstract and phrase using a rule-based component that de-
Modern Standard Arabic (MSA) has a wealth pends on the existence of a dialectal morphologi-
of natural language processing (NLP) tools cal analyzer, a list of morphosyntactic transfer rules,
and resources. In comparison, resources for and DA-MSAdictionaries. The resulting MSA is in
dialectal Arabic(DA),theunstandardizedspo- alattice form that we pass to a language model for n-
ken varieties of Arabic, are still lacking. We bestdecoding. TheoutputofELISSA,whetheratop-
present ELISSA, a machine translation (MT) 1choicesentenceorn-bestsentences,ispassedtoan
system for DA to MSA. ELISSA employs a MSA-English SMT system to produce the English
rule-based approach that relies on morpho- translation sentence. ELISSA-based MSA-pivoting
logical analysis, transfer rules and dictionar- for DA-to-EnglishSMTimprovesBLEUscores(Pa-
ies in addition to language models to produce pineni et al., 2002) on three blind test sets between
MSAparaphrases of DA sentences. ELISSA 0.6% and 1.4%. A manual error analysis of trans-
can be employed as a general preprocessor for lated words shows that ELISSA produces correct
DA when using MSA NLP tools. A man-
ual error analysis of ELISSA’s output shows MSAtranslations over 93% of the time.
that it produces correct MSA translations over The rest of this paper is structured as follows:
93% of the time. Using ELISSA to produce Section 2 motivates the use of ELISSA to improve
MSA versions of DA sentences as part of DA-English SMT with an example. Section 3 dis-
anMSA-pivotingDA-to-EnglishMTsolution, cusses some of the challenges associated with pro-
improves BLEU scores on multiple blind test cessing Arabic and its dialects. Section 4 presents
sets between 0.6% and 1.4%. related work. Section 5 details ELISSA and its
1 Introduction approach and Section 6 presents results evaluating
MuchworkhasbeendoneonModernStandardAra- ELISSA under a variety of conditions.
bic (MSA) natural language processing (NLP) and 2 Motivating Example
machine translation (MT), especially Statistical MT
(SMT). MSA has a wealth of resources in terms of Table 1 shows a motivating example of how pivot-
morphological analyzers, disambiguation systems, ing on MSA can dramatically improve the transla-
and parallel corpora. In comparison, research on di- tion quality of a statistical MT system that is trained
alectal Arabic(DA),theunstandardizedspokenvari- on mostly MSA-to-English parallel corpora. In this
eties of Arabic, is still lacking in NLP in general and example, we use Google Translate’s online Arabic-
1
MTinparticular. In this paper we present ELISSA, English SMT system. The table is divided into two
our DA-to-MSA MT system, and show how it can parts. The top part shows a dialectal (Levantine)
help improve the translation of highly dialectal Ara- sentence, its reference translation to English, and
bic text into English by pivoting on MSA. its Google Translate translation. The Google Trans-
The ELISSA approach can be summarized as fol- late translation clearly struggles with most of the DA
lows. First, ELISSA uses different techniques to words, which were probably unseen in the training
identify dialectal words and multi-word construc- data (i.e., out-of-vocabulary – OOV) and were con-
tions (phrases) in a source sentence. Then, ELISSA 1The system was used on February 21, 2013.
produces MSA paraphrases for the selected words
348
Proceedings of NAACL-HLT 2013, pages 348–358,
c
Atlanta, Georgia, 9–14 June 2013.
2013 Association for Computational Linguistics
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Reference did not tell them when he will go to the country.
Google Bhalhalh Hi Hictpoulo Ahat Profile Tbau not hull Weah Abatln Comintat Anu Mabarhun Oamta welcomed calls
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fy hðh AlHAl¯h ln yktbwA lh ςlý HAyT SfHth AlšxSy¯h wlA yrydwnh Ân yrsl lhm tςlyqAt lÂnh lm yxbrhm mtý syðhb
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Alý Albld.
Google In this case it would not write to him on the wall of his own and do not want to send their comments because he
Translate did not tell them when going to the country.
Table 1: A motivating example for DA-to-English MT by pivoting (bridging) on MSA. The top half of the table
displays a DA sentence, its human reference translation and the output of Google Translate. The bottom half of the
table shows the result of human translation into MSA of the DA sentence before sending it to Google Translate.
sideredpropernouns(transliteratedandcapitalized). person’, one suffix àð- -wn ‘masculine plural’ and
The lack of DA-English parallel corpora suggests one pronominal enclitic Aë+ +hA ‘it/her’. DAs dif-
pivoting on MSA can improve the translation qual- fer from MSA phonologically, morphologically and
ity. In the bottom part of the table, we show a hu- to a lesser degree syntactically. The morpholog-
manMSAtranslationoftheDAsentenceaboveand ical differences are most noticeably expressed in
its Google translation. We see that the results are the use of clitics and affixes that do not exist in
quitepromising. ThegoalofELISSAistomodelthis MSA.Forinstance, the Levantine Arabic equivalent
DA-MSAtranslation automatically. In Section 5.4,
of the MSA example above is AëñJJºJkð w+H+y-
we revisit this example to discuss ELISSA’s perfor- .
ktb-w+hA ‘and they will write it’. The optionality
manceonit. Weshowitsoutput and its correspond- of vocalic diacritics helps hide some of the differ-
ing Google translation in Table 3. ences resulting from vowel changes; compare the
3 Challenges for Processing Arabic and its diacritized forms: Levantine wHayikitbuwhA and
Dialects MSAwasayaktubuwnahA.
All of the NLP challenges of MSA (e.g., optional
Contemporary Arabic is in fact a collection of vari- diacritics and spelling inconsistency) are shared by
eties: MSA, the official language of the Arab World, DA.However,thelackofstandardorthographiesfor
which has a standard orthography and is used in the dialects and their numerous varieties pose new
formal settings; and DAs, the commonly used in- challenges. Additionally, DAs are rather impover-
formal native varieties, which have no standard or- ished in terms of available tools and resources com-
thographies but have an increasing presence on the pared to MSA, e.g., there is very little parallel DA-
web. Arabic, in general, is a morphologically com- English corpora and almost no MSA-DA parallel
plex language which has rich inflectional morphol- corpora. The number and sophistication of morpho-
ogy, expressed both templatically and affixationally, logical analysis and disambiguation tools in DA is
and several classes of attachable clitics. For exam- verylimitedincomparisontoMSA(DuhandKirch-
2 hoff, 2005; Habash and Rambow,2006;AboBakret
ple, the Arabic word AîEñJJºJð w+s+y-ktb-wn+hA
.
al., 2008; Habash, 2010; SalloumandHabash,2011;
‘and they will write it’ has two proclitics (+ð w+ Habash et al., 2012; Habash et al., 2013). MSA
‘and’ and + s+ ‘will’), one prefix -ø y- ‘3rd
tools cannot be effectively used to handle DA, e.g.,
2Arabic transliteration throughout the paper is presented in Habash and Rambow (2006) report that over one-
theHabash-Soudi-Buckwalterscheme(Habashetal.,2007): (in third of Levantine verbs cannot be analyzed using
ˇ
alphabetical order) AbtθjHxdðrzsšSDTDςγfqklmnhwy and the an MSAmorphological analyzer.
ˇ ¯ ˆ
ˆ
additional symbols: ’ Z, Â @, A @, A @, wð', y Zø', ¯h è, ý ø.
349
4 Related Work paraphrases (Callison-Burch et al., 2006; Du et al.,
Dialectal Arabic NLP. Several researchers have 2010). Also related is the work by Nakov and Ng
explored the idea of exploiting existing MSA rich (2011), whousemorphologicalknowledgetogener-
resources to build tools for DA NLP (Chiang et al., ate paraphrases for a morphologically rich language,
2006). Such approaches typically expect the pres- Malay, to extend the phrase table in a Malay-to-
ence of tools/resources to relate DA words to their English SMT system.
MSA variants or translations. Given that DA and Pivoting on MSA or acquiring more DA-English
MSA do not have much in terms of parallel cor- data? Zbibetal.(2012)demonstratedanapproach
pora, rule-based methods to translate DA-to-MSA to cheaply obtaining DA-English data. They used
or other methods to collect word-pair lists have been Amazon’sMechanicalTurk(MTurk)tocreateaDA-
explored. For example, AboBakretal.(2008)intro- English parallel corpus of 1.5M words and added it
duced a hybrid approach to transfer a sentence from to a 150MMSA-Englishparallelcorpustocreatethe
EgyptianArabicintoMSA.Thishybridsystemcon- training corpus of their SMT system. They also used
sisted of a statistical system for tokenizing and tag- MTurk to translate their dialectal test set to MSA
ging, and a rule-based system for constructing dia- in order to compare the MSA-pivoting approach to
critized MSAsentences. Moreover, Al-Sabbagh and the direct translation from DA to English approach.
Girju (2010) described an approach of mining the They showed that even though pivoting on MSA
web to build a DA-to-MSA lexicon. In the context (produced by Humantranslators in an oracle experi-
of DA-to-English SMT, Riesa and Yarowsky (2006) ment)canreduceOOVrateto0.98%from2.27%for
presented a supervised algorithm for online mor- direct translation (without pivoting), it improves by
phemesegmentationonDAthatcuttheOOVwords 4.91% BLEU while direct translation improves by
by half. 6.81%BLEUovertheir12.29%BLEUbaseline(di-
Machine Translation for Closely Related Lan- rect translation using the 150M MSA system). They
guages. Using closely related languages has been concluded that simple vocabulary coverage is not
shown to improve MT quality when resources are sufficient and the domain mismatch is a more im-
ˇ portant problem. The approach we take in this paper
limited. Hajic et al. (2000) argued that for very is orthogonal to such efforts to build parallel data.
close languages, e.g., Czech and Slovak, it is pos- Weplantostudyinteractions between the two types
sible to obtain a better translation quality by using of solutions in the future.
simple methods such as morphological disambigua- OurworkismostsimilartoSawaf(2010)’sMSA-
tion, transfer-based MT and word-for-word MT. pivoting approach. In his approach, DA is normal-
Zhang(1998)introducedaCantonese-MandarinMT ized into MSA using character-based DA normal-
that uses transformational grammar rules. In the ization rules, a DA morphological analyzer, a DA
context of Arabic dialect translation, Sawaf (2010) normalization decoder that relies on language mod-
built a hybrid MT system that uses both statistical els, and a lexicon. Similarly, we use some char-
and rule-based approaches for DA-to-English MT. acter normalization rules, a DA morphological an-
In his approach, DA is normalized into MSA us- alyzer, and DA-MSA dictionaries. In contrast, we
ing a dialectal morphological analyzer. In previ- use hand-written morphosyntactic transfer rules that
ous work, we presented a rule-based DA-MSA sys- focus on translating DA morphemes and lemmas to
tem to improve DA-to-English MT (Salloum and their MSA equivalents.
Habash, 2011; Salloum and Habash, 2012). Our ap- In our previous work (Salloum and Habash, 2011;
proachusedaDAmorphologicalanalyzer(ADAM) Salloum and Habash, 2012), we applied our ap-
and a list of hand-written morphosyntactic transfer proach to tokenized Arabic and our DA-MSA trans-
rules. This use of “resource-rich” related languages fer component used feature transfer rules only. We
is a specific variant of the more general approach did not use a language model to pick the best path;
of using pivot/bridge languages (Utiyama and Isa- instead we kept the ambiguity in the lattice and
hara, 2007; Kumar et al., 2007). In the case of passed it to our SMT system. In contrast, in this pa-
MSA and DA variants, it is plausible to consider per, we run ELISSA on untokenized Arabic, we use
the MSA variants of a DA phrase as monolingual
350
feature, lemma, and surface form transfer rules, and Word-based selection. Methods of this type fall
we pick the best path of the generated MSA lattice in the following categories:
through a language model. a. User token-based selection: The user can mark
Certain aspects of our approach are similar to specificwordsforselectionusingthetag‘/DIA’
Riesa and Yarowsky (2006)’s, in that we use mor- (stands for ‘dialect’) after each word to select.
phological analysis for DA to help DA-English MT; b. Usertype-basedselection: Theusercanspecify
but unlike them, we use a rule-based approach to a list of words to select from, e.g., OOVs. Also
modelDAmorphology. the user can provide a list of words and their
5 ELISSA frequencies and specify a cut-off threshold to
ELISSA is a DA-to-MSA MT System. ELISSA uses prevent selecting a frequent word.
a rule-based approach (with some statistical compo- c. Morphology-based word selection: ELISSA
nents) that relies on the existence of a DA morpho- uses ADAM (Salloum and Habash, 2011)
logical analyzer, a list of hand-written transfer rules, to select words that have DA analyses only
and DA-MSA dictionaries to create a mapping of (DIAONLY)orDA/MSAanalyses(DIAMSA).
DA to MSA words and construct a lattice of pos- d. Dictionary-based selection: ELISSA selects
sible sentences. ELISSA uses a language model to words based on their existence in the DA side
rank and select the generated sentences. of our DA-MSAdictionaries.
ELISSA supports untokenized (raw) input only. e. All: ELISSA selects every word in an input sen-
ELISSAsupportsthreetypesofoutput: top-1choice, tence.
an n-best list or a map file that maps source Phrase-based selection. This selection type uses
words/phrases to target phrases. The top-1 and n- hand-written rules to identify dialectal multi-word
best lists are determined using an untokenized MSA constructions that are mappable to single or multi-
language model to rank the paths in the MSA trans- wordMSAconstructions. Thecurrentcountofthese
lation output lattice. This variety of output types rules is 25. Table 2 presents some rule categories
makesiteasytoplugELISSAwithothersystemsand and related examples.
to use it as a DA preprocessing tool for other MSA In the current version of ELISSA, words can
systems, e.g., MADA (Habash and Rambow, 2005) be selected using either the phrase-based selection
or AMIRA(Diabetal.,2007). method or a word-based selection method, but not
ELISSA’s approach consists of three major steps both. Phrase-based selection has precedence. We
precededbyapreprocessingandnormalizationstep, evaluate different settings for selection step in Sec-
that prepares the input text to be handled (e.g., UTF- tion 6.
8cleaning,Alif/Yanormalization,word-lengthening
normalization), and followed by a post-processing 5.2 Translation
step, that produces the output in the desired form In this step, ELISSA translates the selected words
(e.g., encoding choice). The three major steps are and phrases to their MSA equivalent paraphrases.
Selection, Translation, and Language Modeling. Thespecifictypeofselection determines the type of
5.1 Selection the translation, e.g., phrase-based selected words are
In the first step, ELISSA identifies which words or translated using phrase-based translation rules. The
phrases to paraphrase and which words or phrases MSAparaphrasesarethenusedtoformanMSAlat-
to leave as is. ELISSA provides different methods tice.
(techniques) for selection, and can be configured to Word-based translation. This category has two
usedifferentsubsetsofthem. InSection6weusethe types of translation techniques: surface transla-
term "selection mode" to denote a subset of selec- tion that uses DA-to-MSA surface-to-surface (S2S)
tion methods. Selection methods are classified into transfer rules (TRs) and deep (morphological)trans-
Word-based selection and Phrase-based selection. lation that uses the classic rule-based machine trans-
lation flow: analysis, transfer and generation. The
351
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