Arabic sentence segmentation without punctuation or paragraph breaks requires predicting boundaries from context. The AraSeg Open Track supplies a parallel punctuated view, but its marks do not consistently indicate sentence endings. We train two CAMeLBERT-MSA ensembles, one for the target No-Punctuation No-Paragraph view and one for the auxiliary No-Paragraph view. A discrete rule combines their boundary decisions with punctuation classes projected onto the target words. Model fitting and selection use only the 174-document official training split: five-fold cross-validation selects the training duration, and out-of-fold predictions calibrate the decoders and fusion rule. Development is reserved for evaluation, and the public test is unused. The system achieves 84.61 document-macro F1 on development and 84.88 on the official blind test, with blind precision of 81.87 and recall of 89.50. It predicts too many boundaries in 80.6% of development documents, although some of its lowest scores result from missed boundaries.

In this paper, we introduce a novel approach to generate synthetic data for training Neural Machine Translation systems. The proposed approach supports language variants and dialects with very limited parallel training data. This is achieved using a seed data to project words from a closely-related resource-rich language to an under-resourced language variant via word embedding representations. The proposed approach is based on localized embedding projection of distributed representations which utilizes monolingual embeddings and approximate nearest neighbors queries to transform parallel data across language variants. Our approach is language independent and can be used to generate data for any variant of the source language such as slang or spoken dialect or even for a different language that is related to the source language. We report experimental results on Levantine to English translation using Neural Machine Translation. We show that the synthetic data can provide significant improvements over a very large scale system by more than 2.8 Bleu points and it can be used to provide a reliable translation system for a spoken dialect which does not have sufficient parallel data.