Proceedings of the 2nd Workshop on Speech and Language Processing for Assistive Technologies, pages 101–109,Edinburgh, Scotland, UK, July 30, 2011. c©2011 Association for Computational Linguistics

Arabic Text to Arabic Sign Language Translation System for the Deaf andHearing-Impaired Community

Abdulaziz AlmohimeedUniversity of Southampton

United Kingdomaia07r@ecs.soton.ac.uk

Mike WaldUniversity of Southampton

United Kingdommw@ecs.soton.ac.uk

R. I. DamperUniversity of Southampton

United Kingdomrid@ecs.soton.ac.uk

Abstract

This paper describes a machine translationsystem that offers many deaf and hearing-impaired people the chance to access pub-lished information in Arabic by translatingtext into their first language, Arabic Sign Lan-guage (ArSL). The system was created underthe close guidance of a team that includedthree deaf native signers and one ArSL in-terpreter. We discuss problems inherent inthe design and development of such transla-tion systems and review previous ArSL ma-chine translation systems, which all too oftendemonstrate a lack of collaboration betweenengineers and the deaf community. We de-scribe and explain in detail both the adaptedtranslation approach chosen for the proposedsystem and the ArSL corpus that we collectedfor this purpose. The corpus has 203 signedsentences (with 710 distinct signs) with con-tent restricted to the domain of instructionallanguage as typically used in deaf education.Evaluation shows that the system producestranslated sign sentences outputs with an av-erage word error rate of 46.7% and an averageposition error rate of 29.4% using leave-one-out cross validation. The most frequent sourceof errors is missing signs in the corpus; thiscould be addressed in future by collectingmore corpus material.

1 Introduction

Machine translation (MT) has developed rapidlysince 1947, when Warren Weaver first suggestedthe use of computers to translate natural languages(Augarten, 1984). Presently, this technology offers

a potential chance for ArSL signers to benefit by,for instance, giving them access to texts published inArabic. ArSL and general sign language (SL) haveinherent ambiguity problems that should be takeninto account while designing any ArSL translationsystem. Therefore, ArSL translation must be donethrough close collaboration with the deaf commu-nity and signing experts. This paper describes afull prototype MT system that translates Arabictexts into deaf and hearing-impaired peoples’ firstlanguage, Arabic Sign Language (ArSL). It is theresult of extended collaboration between engineersand a team consisting of three deaf native signersand one ArSL interpreter.

Most existing systems have wrongly assumedthat ArSL is dependent on the Arabic language(Mohandes, 2006; Alnafjan, 2008; Halawani, 2008;Al-Khalifa, 2010). These systems make word-to-sign translations without regard to ArSL’s uniquelinguistic characteristics, such as its own grammar,structure, and idioms, as well as regional variations(Abdel-Fateh, 2004) or translate into finger-spellingsigns that only exist in Arabic, not in ArSL.

This paper begins by providing a brief back-ground of ArSL. It then addresses the problems andmisconceptions plaguing previous ArSL systems.Thereafter, it describes related works built on theassumption of one of the two misconceptions men-tioned above. The rest of the paper will present anexample-based machine translation (EBMT) systemthat translates published Arabic texts to make themaccessible to deaf and hearing-impaired people whouse ArSL.

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2 Background

SL is composed of basic elements of gesture andlocation previously called ‘cheremes’ but modernusage has changed to the even more problematic‘optical phoneme’ (Ojala, 2011). These involvethree components: hand shape (also called handconfiguration), position of the hand in relation tothe signer’s body, and the movement of directionof the hand. These three components are calledmanual features (MFs). In addition, SL may involvenon-manual features (NMFs) that involve other partsof the body, including facial expression, shouldermovements, and head tilts in concurrence with MFs.Unlike written language, where a text expressesideas in a linear sequence, SL employs the spacearound the signer for communication, and the signermay use a combination of MFs and NMFs. Theseare called multi-channel signs. The relationshipbetween multi-channel signs may be parallel, or theymay overlap during SL performance. MFs are basiccomponents of any sign, whereas NMFs play animportant role in composing signs in conjunctionwith MFs. NMFs can be classified into three types interms of their roles. The first is essential: If an NMFis absent, the sign will have a completely differentmeaning.

An example of an essential NMF in ArSL is thesign sentence: “Theft is forbidden”, where as shownin Figure 1(a), closed eyes in the sign for “theft” areessential. If the signer does not close his or her eyes,the “theft” sign will mean “lemon”. The second typeof NMF is a qualifier or emotion. In spoken lan-guage, inflections, or changes in pitch, can expressemotions, such as happiness and sadness; likewise,in SL, NMFs are used to express emotion as inFigure 1(b). The third type of NMF actually plays norole in the sign. In some cases, NMFs remain froma previous sign and are meaningless. Native signersnaturally discard any meaningless NMFs based ontheir knowledge of SL.

3 Problem Definition

ArSL translation is a particularly difficult MT prob-lem for four main reasons, which we now describe.

The first of the four reasons is the lack of linguis-tic studies on ArSL, especially in regard to grammarand structure, which leads to a major misunderstand-

(a) Essential NMF

(b) Emotion NMF

Figure 1: (a) The sign for “theft”, in which the signeruses the right hand while closing his eyes. (b) His facialexpressions show the emotion of the sign.

ing of natural language and misleads researchersinto failing to build usable ArSL translation sys-tems. These misunderstandings about ArSL can besummed up by the following:

• SL is assumed to be a universal language thatallows the deaf anywhere in the world to com-municate, but in reality, many different SLsexist (e.g., British SL, Irish SL, and ArSL).

• ArSL is assumed to be dependent on the Arabiclanguage but it is an independent language thathas its own grammar, structure, and idioms,just like any other natural language.

• ArSL is not finger spelling of the Arabic alpha-bet, although finger spelling is used for namesand places that do not exist in ArSL or forother entities for which no sign exists (e.g.,neologisms).

The related work section will describe anArSL translation system that was built basedon one of these misunderstandings.

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The second factor that should be taken into ac-count while building an ArSL translation system isthe size of the translation corpus, since few linguisticstudies of ArSL’s grammar and structure have beenconducted. The data-driven approach adopted hererelies on the corpus, and the translation accuracy iscorrelated with its size. Also, ArSL does not have awritten system, so there are no existing ArSL doc-uments that could be used to build a translationcorpus, which must be essentially visual (albeit withannotation). Hence, the ArSL corpus must be builtfrom scratch, limiting its size and ability to deliveran accurate translation of signed sentences.

The third problem is representing output signsentences. Unlike spoken languages, which usesounds to produce utterances, SL employs 3D spaceto present signs. The signs are continuous, so somemeans are required to produce novel but fluent signs.One can either use an avatar or, as here, concatenatevideo clips at the expense of fluency.

The last problem is finding a way to evaluateSL output. Although this can be a problem for anMT system, it is a particular challenge here as SLuses multi-channel representations (Almohimeed etal., 2009).

4 Related Works

As mentioned above, we deem it necessary forengineers to collaborate with the deaf communityand/or expert signers to understand some fundamen-tal issues in SL translation. The English to Irish SignLanguage (ISL) translation system developed byMorrissey (2008) is an example of an EBMT systemcreated through strong collaboration between thelocal deaf community and engineers. Her system isbased on previous work by Veale and Way (1997),and Way and Gough (2003; 2005) in which theyuse tags for sub-sentence segmentation. These tagsrepresent the syntactic structure. Their work wasdesigned for large tagged corpora.

However, as previously stated, existing researchin the field of ArSL translation shows a poor or weakrelationship between the Arab deaf community andengineers. For example, the system built by Mohan-des (2006) wrongly assumes that ArSL depends onthe Arabic language and shares the same structureand grammar. Rather than using a data-driven or

rule-based approach, it uses so-called “direct trans-lation” in which words are transliterated into ArSLon a one-to-one basis.

5 Translation System

The lack of linguistic studies on ArSL, especiallyon its grammar and structure, is an additional rea-son to favour the example-based (EMBT) approachover a rule-based methodology. Further, the sta-tistical approach is unlikely to work well giventhe inevitable size limitation of the ArSL corpus,imposed by difficulties of collecting large volumesof video signing data from scratch. On the otherhand, EBMT relies only on example-guided sug-gestions and can still produce reasonable translationoutput even with existing small-size corpora. Wehave adopted a chunk-based EBMT system, whichproduces output sign sentences by comparing theArabic text input to matching text fragments, or‘chunks’. As Figure 2 shows, the system has twophases. Phase 1 is run only once; it pre-compilesthe chunks and their associated signs. Phase 2 isthe actual translation system that converts Arabicinput into ArSL output. The following sections willdescribe each component in Figure 2.

5.1 Google Tashkeel Component

In Arabic, short vowels usually have diacriticalmarks added to distinguish between similar words interms of meaning and pronunciation. For example,the word I.

�J�» means books, whereas I.

�J» means

write. Most Arabic documents are written withoutthe use of diacritics. The reason for this is thatArabic speakers can naturally infer these diacriticsfrom context. The morphological analyser used inthis system can accept Arabic input without diacrit-ics, but it might produce many different analysedoutputs by making different assumptions about themissing diacritics. In the end, the system needs toselect one of these analysed outputs, but it mightnot be equivalent to the input meaning. To solvethis problem, we use Google Tashkeel (http://tashkeel.googlelabs.com/) as a com-ponent in the translation system; this software tooladds missing diacritics to Arabic text, as shownin Figure 3. (In Arabic, tashkeel means “to addshape”.) Using this component, we can guarantee

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Phase 2 Phase 1

Sign Clips

Arabic Text AnnotatedArSLCorpus

Google Tashkeel

Morphological Analyser

RootExtractor Translation

ExamplesCorpus

Search

Alignment

RecombinationDictionary

Translation Unit

Figure 2: Main components of the ArSL chunks-basedEBMT system. Phase 1 is the pre-compilation phase, andPhase 2 is the translation phase.

that the morphological analyser described immedi-ately below will produce only one analysed output.

5.2 Morphological Analyser

The Arabic language is based on root-patternschemes. Using one root, several patterns, andnumerous affixes, the language can generate tens orhundreds of words (Al Sughaiyer and Al Kharashi,2004). A root is defined as a single morpheme

Google Tashkeel

يجب قراءة الشرح

يجب قراءة الشرح

Figure 3: An example of an input and output text usingGoogle Tashkeel. The input is a sentence without dia-critics; the output shows the same sentence after addingdiacritics. English translation: You should read theexplanation.

that provides the basic meaning of a word. InArabic, the root is also the original form of theword, prior to any transformation process (George,1990). In English, the root is the part of the wordthat remains after the removal of affixes. The root isalso sometimes called the stem (Al Khuli, 1982). Amorpheme is defined as the smallest meaningful unitof a language. A stem is a single morpheme or setof concatenated morphemes that is ready to acceptaffixes (Al Khuli, 1982). An affix is a morphemethat can be added before (a prefix) or after (a suffix) aroot or stem. In English, removing a prefix is usuallyharmful because it can reverse a word’s meaning(e.g., the word disadvantage). However, in Arabic,this action does not reverse the meaning of the word(Al Sughaiyer and Al Kharashi, 2004). One of themajor differences between Arabic (and the Semiticlanguage family in general) and English (and similarlanguages) is that Arabic is ‘derivational’ (Al Sug-haiyer and Al Kharashi, 2004), or non-catenative,whereas English is concatenative.

Figure 4 illustrates the Arabic derivational sys-tem. The three words in the top layer (I.

�J», Q�.

g,

I. ëX) are roots that provide the basic meaning of a

word. Roman letters such as ktb are used to demon-strate the pronunciation of Arabic words. After that,in the second layer, “xAxx” (where the small letterx is a variable and the capital letter A is a constant)is added to the roots, generating new words (I.

�KA¿,

QK. A

g, I. ë@X) called stems. Then, the affix “ALxxxx”

is added to stems to generate words (I.�KA¾Ë@, QK. A

mÌ'@,

I. ë@YË@).

Morphology is defined as the grammatical studyof the internal structure of a language, which in-cludes the roots, stems, affixes, and patterns. Amorphological analyser is an important tool forpredicting the syntactic and semantic categories ofunknown words that are not in the dictionary. Theprimary functions of the morphological analyserare the segmentation of a word into a sequence ofmorphemes and the identification of the morpho-syntactic relations between the morphemes (Sem-mar et al., 2005).

Due to the limitation of the ArSL corpus size, thesyntactic and semantic information of unmatchedchunks needs to be used to improve the translationsystem selection, thereby increasing the system’s

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Figure 4: An example of the Arabic derivational system. The first stage shows some examples of roots. An Arabicroot generally contains between 2 and 4 letters. The second stage shows the generated stems from roots after addingthe pattern to the roots. The last stage shows the generated words after the prefixes are added to the stems.

accuracy. To analyse this information, Buckwal-ter’s morphological analyser was used (Buckwalter,2004). In addition, we implemented a root extrac-tor based on a tri-literal root extraction algorithm(Momani and Faraj, 2007). In this work, sentenceswithout diacritics are passed to the morphologicalanalyser, which therefore produces multiple anal-yses (distinguished by different assumptions aboutthe missing diacritics) from which the ‘best’ onemust be chosen. This is not an easy decisionfor a computer system to make. The approachwe have implemented uses the Google Tashkeeloutput in conjunction with the Levenshtein distance(Levenshtein, 1966) to select among the multipleanalyses delivered by Buckwalter’s morphologicalanalyser. Figure 5 gives an example showing howthe morphological and root extractor analyses thesyntactic, semantic and root information.

5.3 Corpus

An annotated ArSL corpus is essential for this sys-tem, as for all data-driven systems. Therefore, wecollected and annotated a new ArSL corpus with thehelp of three native ArSL signers and one expertinterpreter. Full details are given in Almohimeedet al. (2010). This corpus’s domain is restricted tothe kind of instructional language used in schools

يجب قراءة الشرح

VO="يجب" , V= "قراءة", DET="ال", N="شرح"

VO="يجب" Root=NA, V="قراءة" Root="قرأ", DET="ال", N="شرح" Root="شرح"

Morphological Analyser

Root Extractor

Figure 5: An example showing how the morphologicalanalyser and root extractor are utilised for the samesentence as in Fig. 3.

for deaf students. It contains 203 sentences with710 distinct signs. The recorded signed sentenceswere annotated using the ELAN annotation tool(Brugman and Russel, 2004), as shown in Figure 6.Signed sentences were then saved in EUDICO An-notation Format (EAF).

The chunks database and sign dictionary are de-

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Figure 6: An example of a sign sentence annotated by theELAN tool.

rived from this corpus by parsing the EAF file toextract the MFs and NMFs to build a parallel cor-pus of ArSL and associated Arabic chunks. Beforedetecting and extracting chunks, words are linkedwith their equivalent signs in each sentence. After amanual words-to-signs alignment, chunk extractionbegins. This is done automatically by finding con-sistent word/sign sequence pairs. The refined tech-nique proposed by Och and Ney (2003) is employedin this system to extract chunks. Figure 7 illustrateshow the system does so.

The chunks table has four fields. The first con-tains all the Arabic words in the chunk, and thesecond contains an identifier for the video clipsof the signs. The third field contains syntacticand semantic information about the Arabic words.The last field indicates the relative position of theparallel ArSL and text chunks. After extractionof the chunks, the database is sorted from largestchunks (in terms of words) to smallest. Details ofthe tool that carries out these steps will be publishedin a future paper.

5.4 Translation UnitAs depicted earlier in Figure 2, the translation unitcontains three components. The first is the searchcomponent, which is responsible for finding chunksthat match the input. It starts matching words fromthe beginning of the chunks table and scans the

Figure 7: An example of how the system finds chunks byfinding continuous words and signs.

table until the end. Overlapping chunks have higherpriority for selection than separate chunks. Then,for any remaining unmatched input words, it startsmatching stems from the beginning through to theend of the chunks table. The second is the align-ment component, which replaces chunks with theirequivalent signs. For the remaining input words thatdo not have a chunk match, a sign dictionary is usedto translate them. If the word does not appear inthe dictionary (which is possible due to the size ofthe corpus), the system starts searching for the stemof the word and compares it with the stems in thedictionary. If the stem also does not appear in thedatabase or dictionary, the system searches for amatching root. This process will increase the chanceof translating the whole input sentence. The lastcomponent is recombination, which is responsiblefor delivering sign output using the sign location onboth the chunks table and dictionary. The compo-nent will produce a series of sign clips, and betweentwo clips, it will insert a transition clip, as shown inFigure 8.

The output representation has been tested by theteam of three native signers on several hundred

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Figure 8: Image A shows an example of the originalrepresentation, while B shows the output representation.

selected sign sentences in which natural transitionswere replaced by a one-second pause. Moreover,the sign in actual sentences has been replaced bythe equivalent sign in the sign dictionary. Thistest showed that the meaning of the sentences wasclearly expressed to the signers; all three evaluatedthe test sentences by giving them 5 points outof 5, which means the sentence clearly expressesits meaning. In addition, the fluency of sentenceswas deemed acceptable since the evaluators choose4 points out of 5. In view of this positive result, wedid not feel it worthwhile to evaluate the effect ofvariation in (one-second) pause duration, althoughthis will be adjustable by the user in the finalimplementation.

6 Illustration

In this section, we illustrate the workings of theprototype system on three example sentences.

Figures 9, 10, and 11 shows the main stages ofthe translation of Arabic sentence to ArSL for someselected inputs. The input sentence in Figure 9 is2 words, 5 in Figure 10, and 7 in Figure 11. Asshown in the figures, the system starts collecting themorphological details of the Arabic input. Then, itpasses it to the translation unit where it first searchesfor a matching chunk in the chunks table. Whenmany matches are received, the system takes thelargest chunk (recall that the system gives overlap-ping chunks higher priority than isolated chunksand that when no chunks are found in the table,the system uses the stem rather than the word tofind a match). When a match is not found, the

Figure 9: Example translation from the first Arabicsentence to ArSL. The square selection represents achunk match. The crossed arrow means that there wasno chunk match and that it has been translated using thedictionary. In this case, the output is incorrect (Sign5532is missing). English translation: Where do you live?

system uses the dictionary to translate the sign bylooking for the word. In the next stage, alignment,the system identifies the corresponding translationchunk from both the chunks table and dictionary.The system uses the location field in the chunkstable and dictionary to determine the location of thetranslated chunk. The last stage is recombination,during which the system delivers a sign sentence ina Windows Media Video (WMV) format, as shownin Figure 8.

7 Leave-One-Out Cross Validation

The full evaluation results (203 sentences) wereacquired using leave-one-out cross validation. Thistechnique removes a test sentence from the datasetand then uses the remaining dataset as the translationcorpus. The word error rate (WER) was, on average,46.7%, whereas the position-independent word errorrate (PER) averaged 29.4%. The major source of

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Figure 10: Example translation from the second Arabicsentence to ArSL. In this case, the output is correct. En-glish translation: Don’t talk when the teacher is teaching.

Figure 11: Example translation from the third Arabicsentence to ArSL. Again, the output is correct. Englishtranslation: Let the Principal know about any suggestionsor comments that you have.

error is that signs in some translated sentences do nothave equivalent signs in the dictionary. In principle,this source of error could be reduced by collection ofa larger corpus with better coverage of the domain,although this is an expensive process.

8 Conclusion

This paper has described a full working prototypeArSL translation system, designed to give the Ara-bic deaf community the potential to access pub-lished Arabic texts by translating them into theirfirst language, ArSL. The chunk-based EBMT ap-proach was chosen for this system for numerous

reasons. First, the accuracy of this approach iseasily extended by adding extra sign examples tothe corpus. In addition, there is no requirementfor linguistic rules; it purely relies on example-guided suggestions. Moreover, unlike other data-driven approaches, EBMT can translate using even alimited corpus, although performance is expected toimprove with a larger corpus. Its accuracy dependsprimarily on the quality of the examples and theirdegree of similarity to the input text. To over-come the limitations of the relatively small corpus,a morphological analyser and root extractor wereadded to the system to deliver syntactic and semanticinformation that will increase the accuracy of thesystem. The chunks are extracted from a corpus thatcontains samples of the daily instructional languagecurrently used in Arabic deaf schools. Finally, thesystem has been tested using leave-one-out crossvalidation together with WER and PER metrics. Itis not possible to compare the performance of oursystem with any other competing Arabic text toArSL machine translation system, since no othersuch systems exist at present.

Acknowledgments

This work would not have been done without thehard work of the signers’ team: Mr. Ahmed Alzaha-rani, Mr. Kalwfah Alshehri, Mr. Abdulhadi Alharbiand Mr. Ali Alholafi.

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