An automatic diacritization algorithm for undiacritized Arabic text

Modern Standard Arabic (MSA) is used today in most written and some spoken media. It is, however, not the native dialect of any country. Recently, the rate of the written dialectal Arabic text increased dramatically. Most of these texts have been written in the Egyptian dialectal, as it is considered the most widely used dialect and understandable throughout the Middle East. Like other Semitic languages, in written Arabic, short vowels are not written, but are represented by diacritic marks. Nonetheless, these marks are not used in most of the modern Arabic texts (for example books and newspapers). The absence of diacritic marks creates a huge ambiguity, as the un-diacritized word may correspond to more than one correct diacritization (vowelization) form. Hence, the aim of this research is to reduce the ambiguity of the absences of diacritic marks using hybrid algorithm with significantly higher accuracy than the state-of-the-art systems for MSA. Moreover, this research is to implement and evaluate the accuracy of the algorithm for dialectal Arabic text. The design of the proposed algorithm based on two main techniques as follows: statistical n-gram along with maximum likelihood estimation and morphological analyzer. Merging the word, morpheme, and letter levels with their sub-models together into one platform in order to improve the automatic diacritization accuracy is the proposition of this research. Moreover, by utilizing the feature of the case ending diacritization, which is ignoring the diacritic mark on the last letter of the word, shows a significant error improvement. The reason for this remarkable improvement is that the Arabic language prohibits adding diacritic marks over some letters. The hybrid algorithm demonstrated a good performance of 97.9% when applied to MSA corpora (Tashkeela), 97.1% when applied on LDC’s Arabic Treebank-Part 3 v1.0 and 91.8% when applied to Egyptian dialectal corpus (CallHome). The main contribution of this research is the hybrid algorithm for automatic diacritization of undiacritized MSA text and dialectal Arabic text. The proposed algorithm applied and evaluated on Egyptian colloquial dialect, the most widely dialect understood and used throughout the Arab world, which is considered as first time based on the literature review

Take the Hint: Improving Arabic Diacritization with Partially-Diacritized Text

Automatic Arabic diacritization is useful in many applications, ranging from reading support for language learners to accurate pronunciation predictor for downstream tasks like speech synthesis. While most of the previous works focused on models that operate on raw non-diacritized text, production systems can gain accuracy by first letting humans partly annotate ambiguous words. In this paper, we propose 2SDiac, a multi-source model that can effectively support optional diacritics in input to inform all predictions. We also introduce Guided Learning, a training scheme to leverage given diacritics in input with different levels of random masking. We show that the provided hints during test affect more output positions than those annotated. Moreover, experiments on two common benchmarks show that our approach i) greatly outperforms the baseline also when evaluated on non-diacritized text; and ii) achieves state-of-the-art results while reducing the parameter count by over 60%.Comment: Arabic text diacritization, partially-diacritized text, Arabic natural language processin

Neural Arabic Text Diacritization: State of the Art Results and a Novel Approach for Machine Translation

In this work, we present several deep learning models for the automatic diacritization of Arabic text. Our models are built using two main approaches, viz. Feed-Forward Neural Network (FFNN) and Recurrent Neural Network (RNN), with several enhancements such as 100-hot encoding, embeddings, Conditional Random Field (CRF) and Block-Normalized Gradient (BNG). The models are tested on the only freely available benchmark dataset and the results show that our models are either better or on par with other models, which require language-dependent post-processing steps, unlike ours. Moreover, we show that diacritics in Arabic can be used to enhance the models of NLP tasks such as Machine Translation (MT) by proposing the Translation over Diacritization (ToD) approach.Comment: 18 pages, 17 figures, 14 table