Motaz Saad

Motaz Saad, David Langlois, Kamel Smaili

Comparable corpus is a set of topic aligned documents in multiple languages, which are not necessarily translations of each other. These documents are useful for multilingual natural language processing when there is no parallel text available in some domains or languages. In addition, comparable documents are informative because they can tell what is being said about a topic in different languages. In this paper, we present a method to build comparable corpora from Wikipedia encyclopedia and EURONEWS website in English, French and Arabic languages. We further experiment a method to automatically align comparable documents using cross-lingual similarity measures. We investigate two cross-lingual similarity measures to align comparable documents. The first measure is based on bilingual dictionary, and the second measure is based on Latent Semantic Indexing (LSI). Experiments on several corpora show that the Cross-Lingual LSI (CL-LSI) measure outperforms the dictionary based measure. Finally, we collect English and Arabic news documents from the British Broadcast Corporation (BBC) and from ALJAZEERA (JSC) news website respectively. Then we use the CL-LSI similarity measure to automatically align comparable documents of BBC and JSC. The evaluation of the alignment shows that CL-LSI is not only able to align cross-lingual documents at the topic level, but also it is able to do this at the event level.

Salam Thabet Doghmash, Motaz Saad

Hate speech identification in social media has become an increasingly important issue in recent years. In this research, we address two problems: 1) to detect hate speech in Arabic text, 2) to clean a given text from hate speech. The meaning of cleaning here is replacing each bad word with stars based on the number of letters for each word. Regarding the first problem, we conduct several experiments using deep learning models and transformers to determine the best model in terms of the F1 score. Regarding second problem, we consider it as a machine translation task, where the input is a sentence containing dirty text and the output is the same sentence with masking the dirty text. The presented methods achieve the best model in hate speech detection with a 92\% Macro F1 score and 95\% accuracy. Regarding the text cleaning experiment, the best result in the hate speech masking model reached 0.3 in BLEU score with 1-gram, which is a good result compared with the state of the art machine translation systems.