By Abdelhadi Soudi, Ali Farghaly, Günter Neumann, Rabih Zbib
This ebook is the 1st quantity that makes a speciality of the categorical demanding situations of computing device translation with Arabic both as resource or objective language. It properly fills a spot within the literature by means of protecting ways that belong to the 3 significant paradigms of computing device translation: Example-based, statistical and knowledge-based. It offers extensive 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 wonderful researchers from either academia and undefined. it's a welcome and much-needed repository of vital points in Arabic desktop Translation corresponding to morphological research and syntactic reordering, either significant to decreasing the space among Arabic and different languages. many of the proposed innovations also are appropriate to computer translation of Semitic languages except Arabic, in addition to translation of different languages with a fancy morphology.
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6. In particular, one cluster contained over 2,000 lemmaIDs and occurred frequently. Using morphology to improve Example-Based Machine Translation Table 4. ] kitAb_1 wa ‘and’ + kitAb ‘book’ + iy ‘my’ kut~Ab_1 wa ‘and’ + kut~Ab ‘village school’ + iy ‘my’ kAtib_1 wa ‘and’ + kut~Ab ‘authors/ + writers’ iy ‘my’ + ya ‘my’ + ya ‘my’ ktAby kAtib–2 kitAbiy~–1 1 kut~Ab–1 2 2 ktAbyh ktAb kitAb–1 1 2 kAtb kAtib–1 2 Figure 3. Example of clustering. Each ellipse is a lemmaID. Dotted boxes and lines are not part of the graph but are provided to illustrate the Arabic words that result in this graph structure Violetta Cavalli-Sforza & Aaron B.
The MT03 dataset was used to tune parameters controlling the number of translation candidates, length ratio, reorder penalty, language model weighting, and the like. The tuning process evaluated 26 random starting points and then maximized the best starting point through hill-climbing. To avoid inadvertently finding a local maximum, the procedure was done twice on each data set. The parameters were tuned for the baseline system which does not include the morphological generalizations. Weights for the morphological generalizations were determined separately also using the MT03 dataset.
5. Summary and conclusions We have described an approach that improves the output quality of Arabic-toEnglish Example-Based MT by generalizing over morphological features. Our approach takes into consideration two challenges: the need to generalize when faced with data sparseness and the excessive ambiguity that it may result in. Data sparseness occurs with low resource languages due to limited corpora, but the problem is also connected with morphologically complex languages. For the latter, the potential for multiple surface realizations of words makes it less likely that a data-driven translation method will find exact matches to guide translation of a given input.