LAMAD: A Linguistic Attentional Model for Arabic Text Diacritization

In Arabic Language, diacritics are used to specify meanings as well as pronunciations. However, diacritics are often omitted from written texts, which increases the number of possible meanings and pronunciations. This leads to an ambiguous text and makes the computational process on undiacritized text more difficult. In this paper, we propose a Linguistic Attentional Model for Arabic text Diacritization (LAMAD). In LAMAD, a new linguistic feature representation is presented, which utilizes both word and character contextual features. Then, a linguistic attention mechanism is proposed to capture the important linguistic features. In addition, we explore the impact of the linguistic features extracted from the text on Arabic text diacritization (ATD) by introducing them to the linguistic attention mechanism. The extensive experimental results on three datasets with different sizes illustrate that LAMAD outperforms the existing state-of-the-art models.

Cross-lingual Fine-tuning for Abstractive Arabic Text Summarization

While abstractive summarization in certain languages, like English, has already reached fairly good results due to the availability of trend-setting resources, like the CNN/Daily Mail dataset, and considerable progress in generative neural models, progress in abstractive summarization for Arabic, the fifth most-spoken language globally, is still in baby shoes. While some resources for extractive summarization have been available for some time, in this paper, we present the first corpus of human-written abstractive news summaries in Arabic, hoping to lay the foundation of this line of research for this important language. The dataset consists of more than 21 thousand items. We used this dataset to train a set of neural abstractive summarization systems for Arabic by fine-tuning pre-trained language models such as multilingual BERT, AraBERT, and multilingual BART-50. As the Arabic dataset is much smaller than e.g. the CNN/Daily Mail dataset, we also applied cross-lingual knowledge transfer to significantly improve the performance of our baseline systems. The setups included two M-BERT-based summarization models originally trained for Hungarian/English and a similar system based on M-BART-50 originally trained for Russian that were further fine-tuned for Arabic. Evaluation of the models was performed in terms of ROUGE, and a manual evaluation of fluency and adequacy of the models was also performed.

Automatic Prosody Generation for Arabic Text- To - Speech Systems

The main purpose of the present research is to support Arabic Text- to - Speech synthesizers, with natural prosody, based on linguistic analysis of texts to synthesize, and automatic prosody generation, using rules which are deduced from recorded signals analysis, of different types of sentences in Arabic. All the types of Arabic sentences (declarative and constructive) were enumerated with the help of an expert in Arabic linguistics . A textual corpus of about 2500 sentences covering most of these types was built and recorded both in natural prosody and without prosody. Later, these sentences were analyzed to extract prosody effect on the signal parameters, and to build prosody generation rules. In this paper, we present the results on negation sentences, applied on synthesized speech using the open source tool MBROLA. The results can be used with any parametric Arabic synthesizer. Future work will apply the rules on a new Arabic synthesizer based on semi-syllables units, which is under development in the Higher Institute for Applied Sciences and Technology.