Arabic-English translation

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The pervasiveness of coordination in Arabic, with reference to Arabic>English translation

The pervasiveness of coordination in Arabic, with reference to Arabic>English translation

In considering the pervasiveness of coordination in Modern Standard Arabic, this article has attempted to extend the domain of investigation beyond previous studies. By bringing together the findings of such studies, the article has attempted to site them within a wider conceptual framework, with the aim of gaining more integrated insights into why Arabic and English differ in the use of coordination. Thus, while previous studies concentrated on the fact, as a general writing feature, of the greater use of coordination in Arabic than English, particularly to link clauses or sentences, this article has argued that not only is coordination a general feature of Arabic writing, but that various linguistic, textual and ‘rhetorical semantic’ norms work individually – and sometimes in combination – to further entrench coordination as a feature of Modern Standard Arabic. Coordination is thus more ‘hard - wired’ in Modern Standard Arabic than a simple statistical analysis of its relative predominance would suggest. This perspective suggests a new approach to Arabic coordination. This would consider in greater detail (e.g. through larger and more coherent corpora) than in the current study the relationship between the general fact of the prominence of coordination in Arabic and the way this interacts with the linguistic, textual and ‘rhetorical semantic’ norms identified in this article and with other similar norms perhaps still awaiting identification and analysis.
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The Impact of Preprocessing on Arabic English Statistical and Neural Machine Translation

The Impact of Preprocessing on Arabic English Statistical and Neural Machine Translation

Neural networks have become the state- of-the-art approach for machine transla- tion (MT) in many languages. While linguistically-motivated tokenization tech- niques were shown to have significant ef- fects on the performance of statistical MT, it remains unclear if those techniques are well suited for neural MT. In this pa- per, we systematically compare neural and statistical MT models for Arabic-English translation on data preprecossed by vari- ous prominent tokenization schemes. Fur- thermore, we consider a range of data and vocabulary sizes and compare their effect on both approaches. Our empirical re- sults show that the best choice of tokeniza- tion scheme is largely based on the type of model and the size of data. We also show that we can gain significant improvements using a system selection that combines the output from neural and statistical MT.
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An analysis of machine translation errors on the effectiveness of an Arabic-English QA system

An analysis of machine translation errors on the effectiveness of an Arabic-English QA system

source language to the target language. How- ever, most of them are focused on European lan- guage pairs. To our knowledge, only one past example of research has investigated the per- formance of a cross-language Arabic-English QA system Rosso et al (2005). The QA system used by Rosso et al (2005) is based on a system reported in Del Castillo (2004). Their experiment was carried out using the question corpus of the CLEF-2003 competition. They used questions in English and compared the answers with those obtained after the translation back into English from an Arabic question corpus which was manually translated. For the Arabic-English translation process, an automatic machine trans- lator, the TARJIM Arabic-English machine translation system, was used. Rosso el al re- ported a decrease of QA accuracy by more than 30% which was caused by the translation proc- ess.
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Arabic-English Semantic Word Class Alignment to Improve Statistical Machine Translation

Arabic-English Semantic Word Class Alignment to Improve Statistical Machine Translation

Section 2 presents an overview of some recent approaches attempting to introduce semantic fea- tures into the statistical machine translation framework. In Section 3, we describe our method to improve the Arabic-English translation quality. In this section, we first give an overview of the baseline SMT. Then, we present the semantic word clustering algorithm for English and we proceed to directly project the obtained semantic word classes from English side into Arabic side. Finally, we introduce the proposed method to incorporate semantic word classes in SMT. Sec- tion 4 describes the experimental settings and results, which are discussed in the remainder of this Section. Finally, section 5 presents the most relevant conclusions of this work and suggest possible directions for future work.
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The translation of financial terms between English and Arabic, with particular reference to Islamic banking

The translation of financial terms between English and Arabic, with particular reference to Islamic banking

Hamilton’s translation of Al-Hidāyah had a practical purpose for British officials, most of whom did not know Persian or Arabic (at least not the level required to read Al-Hidāyah), providing them with specific information about the functioning of Shariah, and allowing them to understand the activities of Shariah courts. Nonetheless, Strawson (1995) has argued that the ultimate effect – if not the initial purpose – of the translation of Islamic legal texts such as Al-Hidāyah into English was not merely to present Islamic law, but to construct it (Strawson 1995: 1). He argues that over two hundred years a body of knowledge about Islamic law has been built not only in the English language but within Orientalist discourse, quoting Edward Said to stress this point: "Everyone who writes about the Orient must locate himself vis-a-vis the Orient; translated into his text, this location includes the kind of narrative he builds, the kinds of images, themes, motives which circulate in his text – all of which add up to deliberate ways of addressing the reader, containing the Orient and finally, representing it or speaking in its behalf" (Said 1978: 20).
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Translation quality assessment: a situational/textual model for the evaluation of Arabic/English translations

Translation quality assessment: a situational/textual model for the evaluation of Arabic/English translations

This breach of the textual system of the target language may suggest that translation for undergraduate students and some of the postgraduate students, does not go beyond the transfer of[r]

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Relativization in Arabic and English. A case of Translation Problems with Sudanese Students  - A contrastive study -

Relativization in Arabic and English. A case of Translation Problems with Sudanese Students - A contrastive study -

movement began toward the end of the last century in Syria which has reawakened and popularized the old conviction of educated Arabs that ancient Arabia, of pre-Islamic times, which became the classical form of the language in the early centuries of Islam, is better and more correct than any later form. Advocates of this purist doctrine have held that new vocabulary must be derived wholly in accordance with the ancient models or by semantic extension of older forms. They have insisted on the replacement of all foreign loanwords with purely Arabic forms and expressions. The purists have had considerable influence on the development of modern literary Arabic although there has been widespread protest against their extreme point of view. At the same time, and under the increasing influence of Western Civilization, Arab writers and journalists have had to deal with a host of new concepts and ideas previously alien to the Arab way of life. As actual usage demonstrates, the purists have been unable to cope with the sheer bulk of new linguistic material which has had to be incorporated into the language to make it current with advances in world knowledge. The result is seen in the tendency of many writers, especially the fields of science and technology, simply to adapt foreign words from the European languages. Syntax and Morphology of MSA was also subjected to a kind of reform to help accommodate new loan forms into the main stream of the language.
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Improving Arabic Chinese Statistical Machine Translation using English as Pivot Language

Improving Arabic Chinese Statistical Machine Translation using English as Pivot Language

We presented a comparison of two approaches for Arabic-Chinese MT using English as a piv- ot language against direct MT. Our results show that using English as a pivot in either ap- proach outperforms direct translation from Arabic to Chinese. We believe that this is a result of English being a sort of middle ground between Arabic and Chinese in terms of differ- ent linguistic features (in particular word or- der). Our best result is the phrase-pivot system which scores higher than direct translation by 1.1 BLEU points. An error analysis of our sys- tem shows that we successfully handle many complex Arabic-Chinese syntactic variations although there is a large space for improvement still.
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A Formalized Reference Grammar for UNL Based Machine Translation between English and Arabic

A Formalized Reference Grammar for UNL Based Machine Translation between English and Arabic

the incoming UNL document and generate it in natural languages. IAN and Eugene use two types of Natural language dictionaries; enumerative and generative. The enumerative dictionary of IAN contains all inflected word forms of a language together with their corresponding Universal Words (concepts) and a set of linguistic features covering different linguistic levels. The generative dictionary, on the other hand, is the same as the ‘enumerative’ one but it contains all lexemes of language as bases together with a morphological paradigm number that controls the generative morphological behaviour (e.g. agreement and inflected forms) of words in natural language (Martins and Avetisyan 2009). It might be a fact that all languages have classical reference grammars in grammar books. Such a reference grammar maybe defined as a description of the grammar of a language, with explanations of the principles governing the construction of words, phrases, clauses, and sentences. It is designed to give someone a reference tool for looking up specific details of the language. In Natural Language Processing, computers should also learn a language in order to give a comprehensive and objective test-bed that enables us to evaluate, compare and follow up the performance of different grammars. A formalized reference grammar is needed in order to synchronize different languages; the UNL is initiating this idea as it utilizes a standardized environment. The current paper is limited to English and Arabic only; it is organized as follows: Section 1 discusses the design and compilation of the reference corpus. Section 2 discusses the design and implementation of the analysis grammar. Section 3 discusses the design and implementation of the generation grammar. Section 4 evaluates the analysis and generation results in English and Arabic. And finally section 5 is a conclusion and future work. 1 Reference corpus
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A POS based preordering approach for English to Arabic statistical machine translation

A POS based preordering approach for English to Arabic statistical machine translation

For the Arabic language, our preprocessing includes: diacritic sign removal, Ara- bic character normalization and word segmentation by means of the AMIRA toolkit [19] using the default tokenization scheme in which conjunctions, prepo- sitions, determinants, suffixes and future markers are all individually separated. For the English side, only word tokenization is performed using the Python NLTK toolkit 4 . We have also added a number <nbr> and a link classes <url> to all numbers and links found in the parallel corpus respectively. Sentence length has been limited to 40 words; ”bad” sentence pairs, i.e. whose length difference exceeds a certain threshold were also removed.
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Unsupervised Word Segmentation Improves Dialectal Arabic to English Machine Translation

Unsupervised Word Segmentation Improves Dialectal Arabic to English Machine Translation

Collecting resources for dialectal Arabic: Several researchers have directed efforts to de- velop DA computational resources (Maamouri et al., 2006; Al-Sabbagh and Girju, 2010; Zaidan and Callison-Burch, 2011; Salama et al., 2014). Zbib et al. (2012) built two dialectal Arabic-English parallel corpora for Egyptian and Levantine Ara- bic using crowdsourcing. Bouamor et al. (2014) presented a multi-dialectal Arabic parallel corpus, which covers five Arabic dialects besides MSA and English. Mubarak and Darwish (2014) col- lected a multi-dialectal corpus using Twitter. Un- like previous work, we focus on Gulf subdialects, particularly Qatari Arabic. The monolingual data that we collected is a high-quality dialectal re- source and originates from dialect-specific sources such as novels and forums.
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Integrating morpho syntactic features in English Arabic statistical machine translation

Integrating morpho syntactic features in English Arabic statistical machine translation

cally-informed modeling approach than the n- gram one. FLM are an extension of standard lan- guage model where the prediction is based upon a set of features (and not only on previous occur- rences of the predicted word). FLM addresses the problems of data-sparsity in morphologically complex languages by representing words as bundles of features, thus one can easily capture dependencies between subword parts of adjacent words. Some other works have been proposed to integrate linguistic information such as part-of- speech, morphology and shallow syntax in con- ventional phrase-based statistical translation (Koehn and Hoang. 2007). These translation models allow integrating multiple levels of in- formation into the translation process instead of incorporating linguistic markers in either prepro- cessing or postprocessing steps. For example, in morphologically rich languages it may be prefer- able to translate lemma, part-of-speech and mor- phological information separately and combine the information on the target side to generate the output surface words. In this model the transla- tion process is broken up into three steps. Trans- late input lemmas into output lemmas in a first step. Then, translate morphological and POS fac- tors in a second step. Finally, generate surface forms given the lemma and the linguistic factors. These factored translation models have been used to improve the word level translation accu- racy by incorporating the factors in phrase-based translation. In (Schwenk and Déchelotte. 2007), authors focus on incorporating morpho-syntactic features in the translation model for the English- Spanish machine translation process. In this work, authors propose the use of augmented units in the translation model instead of simple words. These units are composed by surface word forms combined with their morpho- syntactic categories. This method allows lexical disambiguation of words using their roles and their grammatical contexts.
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Rule Based and Expectation Maximization algorithm for Arabic-English Hybrid Machine Translation

Rule Based and Expectation Maximization algorithm for Arabic-English Hybrid Machine Translation

In this system the lexicon is accountable for inferring morphological and classifying verbs, nouns, adverb and adjectives when needed. It is the main lexicon translation; the source language searches in a dictionary and then chooses the translation. A lexicon provides the specific details about every individual lexical entry (i.e. word or phrase) in the vocabulary of the language concerned. Lexicon contains grammatical information which usually have abbreviated form: ‘n’ for noun, ‘v’ for verb, ‘pron’ for pronoun, ‘det’ for determiner, ‘prep’ for preposition,’adj’ for adjective, ‘adv’ for adverb, and ‘conj’ for conjunction. The lexicon must contain information about all the different words that can be used. If the word is ambiguous, it will be described by multiple entries in the lexicon, one for each different use.
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The Arabic translation of Theodosius's Sphaerica

The Arabic translation of Theodosius's Sphaerica

It would, not "be out of place, in closing, to make a few observa­ tions on the Arabic version of Sphaerica presented, in this thesis. If it can be assumed that the growth of technical terminology within a language follows a course leading from prolixity to compression, it might be said that the version of Sphaerica herein is much closer to, if not a copy of, the actual translation of the text than are the other versions presented in appendix six. This means that the version herein can be seen as more useful for the reconstruction of Theodosius’s text than the later versions. Certainly, the
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Machine Translation of Arabic Dialects

Machine Translation of Arabic Dialects

Existing work on natural language processing of Di- alectal Arabic text, including machine translation, is somewhat limited. Previous research on Dialectal Arabic MT has focused on normalizing dialectal in- put words into MSA equivalents before translating to English, and they deal with inputs that contain a limited fraction of dialectal words. Sawaf (2010) normalized the dialectal words in a hybrid (rule- based and statistical) MT system, by performing a combination of character- and morpheme-level map- pings. They then translated the normalized source to English using a hybrid MT or alternatively a Statistical MT system. They tested their method on proprietary test sets, observing about 1 BLEU point (Papineni et al., 2002) increase on broadcast news/conversation and about 2 points on web text. Salloum and Habash (2011) reduced the proportion of dialectal out-of-vocabulary (OOV) words also by mapping their affixed morphemes to MSA equiva- lents (but did not perform lexical mapping on the word stems). They allowed for multiple morpho- logical analyses, passing them on to the MT system in the form of a lattice. They tested on a subset of broadcast news and broadcast conversation data sets consisting of sentences that contain at least one re- gion marked as non-MSA, with an initial OOV rate against an MSA training corpus of 1.51%. They obtained a 0.62 BLEU point gain. Abo Bakr et al. (2008) suggested another hybrid system to map Egyptian Arabic to MSA, using morphological anal- ysis on the input and an Egyptian-MSA lexicon.
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Improving Arabic to English Statistical Machine Translation by Reordering Post Verbal Subjects for Alignment

Improving Arabic to English Statistical Machine Translation by Reordering Post Verbal Subjects for Alignment

We study the challenges raised by Ara- bic verb and subject detection and re- ordering in Statistical Machine Transla- tion (SMT). We show that post-verbal sub- ject (VS) constructions are hard to trans- late because they have highly ambiguous reordering patterns when translated to En- glish. In addition, implementing reorder- ing is difficult because the boundaries of VS constructions are hard to detect accu- rately, even with a state-of-the-art Arabic dependency parser. We therefore propose to reorder VS constructions into SV or- der for SMT word alignment only. This strategy significantly improves BLEU and TER scores, even on a strong large-scale baseline and despite noisy parses.
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A bilingual emotional advantage? An investigation into the effects of psychological factors in emotion perception in Arabic and in English of Arabic English bilinguals and Arabic /English monolinguals

A bilingual emotional advantage? An investigation into the effects of psychological factors in emotion perception in Arabic and in English of Arabic English bilinguals and Arabic /English monolinguals

Using two languages is an exercise through which the brain develops different cognitive (de Bot, 2017) and socio-emotional skills (Han, 2010) beyond the linguistic domain. The BA defined in lay terms refers to the “skills areas in which bilinguals outperform monolinguals (de Bot, 2017, p. 15). A pioneer in this area is Ellen Bialystok who attributed the BA to the plasticity of bilinguals’ cognitive systems which is the consequence of using and switching between the L1 and L2 (Bialystok, 2011). It also can be attributed to the bilinguals’ consistent need to monitor and manage the use of their two languages (Prior & MacWhinney, 2010). A recent meta-analysis of 152 studies and 891 comparisons of bilinguals’ and monolinguals’ performance in six executive functioning domains by Lehtonen, Soveri, Laine, Järvenpää, de Bruin and Antfolk (2018) confirmed de Bot’s (2017) observation that the existence of the BA is in doubt. The authors concluded that their analysis does not provide strong evidence for the benefits of bilingualism in cognitive control functions in adulthood due to the very small effect sizes observed in studies on inhibitory control, shifting, and WM disappeared, specifically when reducing publication bias (Lehtonen et al., 2018). Cape, Vega-Mendoza, Bak and Sorace (to appear) suggested that one of the reasons for the small effect sizes in measures of executive control is that other factors play a role such as context of bilingualism experience. Based on their analysis of executive functions of primary school pupils in Gaelic Medium Education and English Medium Education in Scotland, the authors suggest that bilinguals who do not switch much between their languages may not experience a cognitive effect.
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The Effect of Using Mobiles in Students' Ability in Translation from English into Arabic at Jadara University in Jordan

The Effect of Using Mobiles in Students' Ability in Translation from English into Arabic at Jadara University in Jordan

The popularization of the computer and its technology had led very quickly to use it for translation. It is used through the appearance of useful translator tools including translation memories, the translation management programs, electronic corpora, and so forth (Matusov et al. 2006). They continue that the breakthrough happening in the computer technologies may lead to new theories in translation to be more functional than paper and pen translation. In other words, E-learning such as Machine translation (MT) and Computer-Assisted Translation (CAT) are intended to replace translators. Software in MT aims at assembling all the information necessary for translation in one program so that a text can be translated. Sim et al (2007), on the other hand, indicate that the sector of communities stimulated the demand for electronic translation leading to the development in countries such as France, Germany, Canada and Japan of new translation systems from one language to another. With global interconnectivity, modern day activities are fuelling the growth of the translation because CAT responds more realistically to translation and supporting actual needs since it
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Chunk Based Verb Reordering in VSO Sentences for Arabic English Statistical Machine Translation

Chunk Based Verb Reordering in VSO Sentences for Arabic English Statistical Machine Translation

We trained a Moses-based system on a subset of the NIST-MT09 Evaluation data 4 for a total of 981K sentences, 30M words. We first aligned the data with GIZA++ and use the resulting Intersec- tion set to apply the technique explained in Sect. 2. We then retrained the whole system – from word alignment to phrase scoring – on the reordered data and evaluated it on two different versions of Eval08-NW: plain and oracle verb-reordered, ob- tained by exploiting word alignments with the first of the four available English references. The first experiment is meant to measure the impact of the verb reordering procedure on training only. The latter will provide an estimate of the maximum im- provement we can expect from the application to the test of an optimal verb reordering prediction technique. Given our experimental setting, one could argue that our BLEU score is biased because one of the references was also used to generate the verb reordering. However, in a series of exper- iments not reported here, we evaluated the same systems using only the remaining three references and observed similar trends as when all four refer- ences are used.
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A relevance theoretic account of the translation of ideological assumptions in the language of the news with specific reference to translation from English into Arabic

A relevance theoretic account of the translation of ideological assumptions in the language of the news with specific reference to translation from English into Arabic

By drawing on the findings of our relevance-theoretic model, examples of translation of modality in news articles illustrate that translating an English modal into an Arabic modal that d[r]

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