By Abdelhadi Soudi, Ali Farghaly, Günter Neumann, Rabih Zbib
This publication is the 1st quantity that specializes in the explicit demanding situations of computer translation with Arabic both as resource or goal language. It well fills a spot within the literature by means of protecting methods that belong to the 3 significant paradigms of computer translation: Example-based, statistical and knowledge-based. It offers wide yet rigorous insurance of the equipment for incorporating linguistic wisdom into empirical MT. The publication brings jointly unique and prolonged contributions from a bunch of unique researchers from either academia and undefined. it's a welcome and much-needed repository of significant elements in Arabic desktop Translation akin to morphological research and syntactic reordering, either significant to lowering the gap among Arabic and different languages. lots of the proposed innovations also are acceptable to computer translation of Semitic languages except Arabic, in addition to translation of different languages with a fancy morphology.
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Extra info for Challenges for Arabic Machine Translation
Additional resources There are a variety of Arabic resources at LDC that do not fit into the above categories but might be of use to researchers in machine translation. Among o thers, these include the OntoNotes corpora and the Egyptian Colloquial Arabic Lexicon. The OntoNotes project includes Arabic PropBank, word sense, coreference and name annotation. The advantages of parallel PropBanks and semantic role labeling for machine translation have been suggested in Wu et al. (2010) and Lo & Wu (2010).
Conceptually, the most similar work to ours is that of (Nießen & Ney 2004) and (Yang & Kirchhoff 2006). (Nießen & Ney 2004) describe an SMT system that uses a generalized hierarchical lexicon with morphological features for German-English translation. (Yang & Kirchhoff 2006) describe a statistical system that generalizes over the phrase table through a back-off model with examples in German-English and Finnish-English translation; however, their work only uses stemming and compound splitting and no other generalization over individual words.
This is a stricter constraint than the one that was placed on matching in Phase 1 of the work, but a necessary one for doing adaptation on the target side. Features such as gender and case are allowed to be different because, most of the time, these features have the same realization in English. Even if the realization is occasionally different, it results in an acceptable translation and the LM will select the most likely combination of 7. org/mcl/. 8. As the process is external to the EBMT system, a different morphological analyzer could be used, so the technique could be easily applied to a different language.
Challenges for Arabic Machine Translation by Abdelhadi Soudi, Ali Farghaly, Günter Neumann, Rabih Zbib