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4.3.3 Interlingua 4.3. BASIC MACHINE TRANSLATION STRATEGIES The Interlingua approach is to develop a universal language-representation for text. In effect, in Interlingua there is no transfer map, and the MT model thus has phases: analysis and generation. In a standard multilingual system with X source languages and Y target languages, the transfer approach will involve XY transfer maps; moreover, we need X analysers and Y generators. In the Interlingua approach, only X parsers and Y generators are needed per language. Interlingua based MT is done via an intermediary (semantic) representation of the source language text. Interlingua is supposed to be a language independent representation from which translations can be generated to different target languages. Translation needs two phases: analysis from the source language to the Interlingua (universal language) and generation from the universal language to the target language. An Interlingua translation model with eight languages is shown in Figure 4.2. Figure 4.2: Interlingua1 model with eight languages pairs 52
4.3. BASIC MACHINE TRANSLATION STRATEGIES To apply our framework to other generation languages, we only need to change the generation phases. The intermediate representation is independent of the target language, and this is the benefit of using an Interlingua approach, since analysis and generation are separate tasks which are implemented independently. 4.3.4 Transfer systems Transfer systems are a middle course between direct and Interlingua MT strategies. Transfer systems divide translation into steps which clearly differentiate source language and target language parts. In the transfer approach there is therefore no language- independent representation: the source language intermediate representation is specific to a particular language, as is the target language intermediate representation. There is no necessary equivalence between the source and target intermediate representations for the same language. In the transfer strategy a source language sentence is first parsed into an internal representation. Thereafter a transfer is made at both lexical and structural levels into equivalent structures of the target language. In the third stage a translation is generated. Whereas the Interlingua approach requires complete resolution of all ambiguities in the source language text so that translation into any other language is possible, in the transfer approach only those ambiguities inherent in the language in question are tackled. This approach is a development over direct translation and this was lexically driven. The level of transfer differs from system to system - the representation varies from only syntactic deep structure to syntactic-semantic interpret trees. A multilingual transfer model with eight languages pairs is presented in Figure 4.3. In comparison with the Interlingua system there are clear disadvantages in the transfer approach. The addition of a new language involves not only the two modules for analysis and generation, but also the addition of new transfer modules, the number of which 53
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A generic framework for Arabic to E
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sentence and its linking algorithm
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Declaration I herby certify that th
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advices, but also academically and
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CONTENTS 2.3.1.2 Indefinite nouns .
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CONTENTS 4.4.1 Non-Roman alphabet s
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CONTENTS 7.2.8 Limitation of UniAra
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LIST OF FIGURES 4.4 Difference betw
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List of Tables 2.1 Dual: merely add
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LIST OF TABLES 5.9 Test strategy: p
Page 21 and 22: 1 Introduction The following paragr
Page 23 and 24: high-quality translation technology
Page 25 and 26: 1.3. TECHNOLOGIES ditransitive). Th
Page 27 and 28: 1.4. THESIS ORGANIZATION 2009b). Ch
Page 29 and 30: 2.1. CHARACTERISTICS OF THE ARABIC
Page 31 and 32: 2.2 Characteristics of Arabic words
Page 33 and 34: 2.2. CHARACTERISTICS OF ARABIC WORD
Page 35 and 36: 2.3. PART OF SPEECH INVENTORY OF TH
Page 37 and 38: 2.3.4 Verbs 2.3. PART OF SPEECH INV
Page 39 and 40: 2.3.4.2 Aspect 2.3. PART OF SPEECH
Page 41 and 42: 2.3. PART OF SPEECH INVENTORY OF TH
Page 43 and 44: 2.4.1.1 Verb and noun 2.4. SENTENCE
Page 45 and 46: Table 2.27: verb(V), subject(S) and
Page 47 and 48: 3 Role and Reference Grammar (RRG)
Page 49 and 50: 3.1. ROLE AND REFERENCE GRAMMAR LIN
Page 51 and 52: 3.2. FORMAL REPRESENTATION OF LAYER
Page 57 and 58: 3.3. NOUN PHRASE STRUCTURE appropri
Page 59 and 60: 3.4. LEXICAL REPRESENTATIONS FOR VE
Page 61 and 62: 3.5. WHY WE USE RRG AS THE LINGUIST
Page 63 and 64: 3.5. WHY WE USE RRG AS THE LINGUIST
Page 65 and 66: 3.6. SUMMARY there will be more com
Page 67 and 68: 4.1. ADVANTAGES OF MACHINE TRANSLAT
Page 69 and 70: 4.2.3 Lexical databases 4.2. COMPUT
Page 71: 4.3.2 Direct translation 4.3. BASIC
Page 75 and 76: would also be needed for an interli
Page 77 and 78: 4.4.1 Non-Roman alphabet scripts 4.
Page 79 and 80: Figure 4.5: NP rule (NP -> det n pp
Page 81 and 82: 4.5. CHALLENGES OF ARABIC TO ENGLIS
Page 83 and 84: Figure 4.7: Direct MT system 4.6.2
Page 85 and 86: 4.7 Summary Figure 4.9: Structure t
Page 87 and 88: 5 Design of Arabic to English machi
Page 89 and 90: 5.2. DESIGNING AN XML LEXICON ARCHI
Page 95 and 96: 5.3. DESIGN OF TEST STRATEGY Table
Page 97 and 98: 5.4. DESIGN OF EVALUATION CRITERIA
Page 99 and 100: 6 UniArab: a proof-of-concept Arabi
Page 101 and 102: 6.1. CONCEPTUAL STRUCTURE OF THE UN
Page 107 and 108: 6.2. UNIARAB: LEXICAL REPRESENTATIO
Page 113 and 114: 6.3. UNIARAB: GENERATION which mean
Page 115 and 116: Figure 6.11: UniArab’s GUI 1 6.4.
Page 117 and 118: 6.4.1 Lexicon interface Figure 6.13
Page 119 and 120: 6.6 Summary 6.6. SUMMARY We present
Page 121 and 122: 7.2. SENTENCE TESTS to which standa
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Table 7.2: Test : Verb-subject; agr
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Figure 7.4: Verb-subject agreement
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Table 7.5: Test : gender-ambiguous
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Table 7.7: Test : Verb ‘to be’
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Table 7.9: Test : Verb ‘to have
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7.2. SENTENCE TESTS Table 7.11: Tes
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7.2.6 Pro-drop Arabic Table 7.13: T
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Table 7.15: Test : Intransitive 2 A
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7.2.7.3 Ditransitive Table 7.17: Te
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Table 7.19: Test : Ditransitive wit
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7.2. SENTENCE TESTS In a case where
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7.3 System evaluation 7.3. SYSTEM E
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7.3. SYSTEM EVALUATION search engin
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Now is not the end. It is not the b
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8.1. THESIS SUMMARY and so we hope
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and proving the suitability of the
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References Abn-Aqeal, 2007. sharah
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REFERENCES Salem, Y., Hensman, A.,
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Appendix 139
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• Yasser Salem and Brian Nolan. 2
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Arabic Letter and Phonetic Value Le
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C List of translatable sentences I
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she is an engineer. Zaid loves Fati
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Jack is eating with the spoon.
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Ayah tore the page. ֓āyh
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Eman saw Sarah. r֓at ֓iymā
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Carl is brushing his hair. ym
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I want a book. ֓aryd ktāb
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Omar gave the book to Khalid.
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D Verbs in lexicon Verbs in Arabic
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Arabic Example Logical Structure
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Arabic Example Logical Structure
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Arabic ys. rs. r Example the wheel
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Arabic ֓adrs Example I am teach
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Package: Name of Class Class Summar
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