Challenges for Arabic Machine Translation by Abdelhadi Soudi, Ali Farghaly, Günter Neumann, Rabih Zbib

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By Abdelhadi Soudi, Ali Farghaly, Günter Neumann, Rabih Zbib

This booklet is the 1st quantity that specializes in the explicit demanding situations of computing device translation with Arabic both as resource or goal language. It properly fills a spot within the literature by way of overlaying ways that belong to the 3 significant paradigms of laptop translation: Example-based, statistical and knowledge-based. It presents extensive yet rigorous assurance of the equipment for incorporating linguistic wisdom into empirical MT. The e-book brings jointly unique and prolonged contributions from a bunch of amazing researchers from either academia and undefined. it's a welcome and much-needed repository of significant points in Arabic laptop Translation equivalent to morphological research and syntactic reordering, either critical to decreasing the gap among Arabic and different languages. many of the proposed thoughts also are appropriate to computer translation of Semitic languages except Arabic, in addition to translation of alternative languages with a posh morphology.

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Adding morphology to EBMT for Arabic-to-English translation We turn our attention now to the core topic of this chapter, how morphology can be used to improve EBMT for Arabic to English translation. We reiterate the data sparseness challenge faced by data-driven systems in general, and in translating Using morphology to improve Example-Based Machine Translation  from Arabic in particular. Bar & Dershowitz (Chapter 4) reproduce for Arabic the results previously obtained for Spanish by Callison-Burch (2007): while the percentage of word sequences (n-grams) covered increases with corpus size, even with very large corpora most sequences longer than one word remain uncovered.

1995), the first large scale publicly available parallel corpus. SMT systems require large parallel corpora in order to estimate accurately the parameters of the underlying statistical models and, to the extent that larger corpora mean more l­ anguage coverage, the larger the better Callison-Burch (2007). g. Brown 1996). The final reason for choosing EBMT might just be that there is no reason for everybody to pursue a single MT approach. While EBMT has not been a ­popular approach in the United States – the only research group working with that approach was indeed ours – it has received substantially more attention in Europe and Japan, among other places.

Our work addresses specifically this issue: we aim to transform the input to be translated into a more general class whose membership includes larger portions of the training corpus, so that the system has more examples from which to choose the best translation. This strategy increases the coverage of the available examples and captures things that are not directly seen in the text. Even if an exact translation cannot be found in the corpus, a generalized translation can still be largely correct and is superior to no translation at all.

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