Atar: Attention-based LSTM for Arabizi transliteration

A non-standard romanization of Arabic script, known as Arbizi, is widely used in Arabic online and SMS/chat communities. However, since state-of-the-art tools and applications for Arabic NLP expects Arabic to be written in Arabic script, handling contents written in Arabizi requires a special attention either by building customized tools or by transliterating them into Arabic script. The latter approach is the more common one and this work presents two significant contributions in this direction. The first one is to collect and publicly release the first large-scale “Arabizi to Arabic script” parallel corpus focusing on the Jordanian dialect and consisting of more than 25 k pairs carefully created and inspected by native speakers to ensure highest quality. Second, we present Atar, an attention-based encoder-decoder model for Arabizi transliteration. Training and testing this model on our dataset yields impressive accuracy (79%) and BLEU score (88.49)

The effect of using same language subtitling (SLS) in content comprehension and vocabulary acquisition in Arabic as a foreign language (AFL)

This study investigates the effects of SLS (Same-Language subtitling) on content comprehension and vocabulary acquisition of MSA (Modern Standard Arabic) as L2 at the intermediate level and addresses three research questions: (1) Does SLS enhance or hinder L2 content comprehension when the writing script of L2 is different than that of L1? (2) Does SLS enhance or hinder L2 vocabulary acquisition when the writing script of L2 is different than that of L1? (3) What is students\u27 attitude towards the use of SLS? Twenty seven students of AUC\u27s ALI program with English as L1 were chosen for the study and divided between a control group (without SLS) and a treatment group (with SLS). Instructions concerning the procedure were given in L1 to rule out any false results due to task misinterpretation. Both groups watched an authentic 3:11 minute documentary twice after completing an individual background questionnaire and taking a multiple choice vocabulary pre-viewing test. Post-viewing, participants took the same vocabulary test, then wrote a summary in L1 based on their notes taken during/and in-between the viewings and completed a questionnaire/questions related to their experience with and -/out captions. L1 summaries were analyzed in terms of 23 semantic units related to content comprehension. The degree of vocabulary acquisition was calculated by comparing the responses between the pre- and post-viewing vocabulary multiply choice tests. Results using t-test and one way ANOVA indicate that SLS neither facilitates nor hinders comprehension and vocabulary acquisition. The majority of students enjoyed captions and wanted to continue using them in class. Pedagogical suggestions and future research recommended training with captions and focus on other text- aids, e.g. reverse subtitling at other proficiency levels. Contains 84 references, 17 figures, 5 pictures and 5 tables

SenZi: A Sentiment Analysis Lexicon for the Latinised Arabic (Arabizi)

Arabizi is an informal written form of dialectal Arabic transcribed in Latin alphanumeric characters. It has a proven popularity on chat platforms and social media, yet it suffers from a severe lack of natural language processing (NLP) resources. As such, texts written in Arabizi are often disregarded in sentiment analysis tasks for Arabic. In this paper we describe the creation of a sentiment lexicon for Arabizi that was enriched with word embeddings. The result is a new Arabizi lexicon consisting of 11.3K positive and 13.3K negative words. We evaluated this lexicon by classifying the sentiment of Arabizi tweets achieving an F1-score of 0.72. We provide a detailed error analysis to present the challenges that impact the sentiment analysis of Arabizi

Multi-Task sequence prediction for Tunisian Arabizi multi-level annotation

In this paper we propose a multi-task sequence prediction system, based on recurrent neural networks and used to annotate on multiple levels an Arabizi Tunisian corpus. The annotation performed are text classification, tokenization, PoS tagging and encoding of Tunisian Arabizi into CODA* Arabic orthography. The system is learned to predict all the annotation levels in cascade, starting from Arabizi input. We evaluate the system on the TIGER German corpus, suitably converting data to have a multi-task problem, in order to show the effectiveness of our neural architecture. We show also how we used the system in order to annotate a Tunisian Arabizi corpus, which has been afterwards manually corrected and used to further evaluate sequence models on Tunisian data. Our system is developed for the Fairseq framework, which allows for a fast and easy use for any other sequence prediction problem

Expressive Speech Act in Comments on Instagram BBC Arabic

A language reflects the culture of the speakers. The use of language on social media platforms without an editor creates several issues, one of which is that it can spiral out of control. The goal of this research is to categorize and analyze expressive speech acts of praise and criticism in BBC Arabic Instagram comments. This is a descriptive-qualitative study with data sources in the form of netizen comments on BBC Arabic between January and February. There are two hundred data in total and they were obtained using a purposive sample technique. According to the findings of this research, many users of social media express criticism more often than they express praise or appreciation, with a ratio of 87:13. Being outspoken in their criticism will become the character and culture of social media users over time. This is clearly not in line with Arab culture, where Islam is the dominant religion. As a result, that was expected that research based on this model will be able to influence how language is used on social media

A survey on author profiling, deception, and irony detection for the Arabic language

"This is the peer reviewed version of the following article: [FULL CITE], which has been published in final form at [Link to final article using the DOI]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving."[EN] The possibility of knowing people traits on the basis of what they write is a field of growing interest named author profiling. To infer a user's gender, age, native language, language variety, or even when the user lies, simply by analyzing her texts, opens a wide range of possibilities from the point of view of security. In this paper, we review the state of the art about some of the main author profiling problems, as well as deception and irony detection, especially focusing on the Arabic language.Qatar National Research Fund, Grant/Award Number: NPRP 9-175-1-033Rosso, P.; Rangel-Pardo, FM.; Hernandez-Farias, DI.; Cagnina, L.; Zaghouani, W.; Charfi, A. (2018). 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