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While resources for English language are fairly sufficient to understandcontent on social media, similar resources in Arabic are still immature. Themain reason that the resources in Arabic are insufficient is that Arabic hasmany dialects in addition to the standard version (MSA). Arabs do not use MSAin their daily communications; rather, they use dialectal versions.Unfortunately, social users transfer this phenomenon into their use of socialmedia platforms, which in turn has raised an urgent need for building suitableAI models for language-dependent applications. Existing machine translation(MT) systems designed for MSA fail to work well with Arabic dialects. In lightof this, it is necessary to adapt to the informal nature of communication onsocial networks by developing MT systems that can effectively handle thevarious dialects of Arabic. Unlike for MSA that shows advanced progress in MTsystems, little effort has been exerted to utilize Arabic dialects for MTsystems. While few attempts have been made to build translation datasets fordialectal Arabic, they are domain dependent and are not OSN cultural-languagefriendly. In this work, we attempt to alleviate these limitations by proposingan online social network-based multidialect Arabic dataset that is crafted bycontextually translating English tweets into four Arabic dialects: Gulf,Yemeni, Iraqi, and Levantine. To perform the translation, we followed ourproposed guideline framework for content translation, which could beuniversally applicable for translation between foreign languages and localdialects. We validated the authenticity of our proposed dataset by developingneural MT models for four Arabic dialects. Our results have shown a superiorperformance of our NMT models trained using our dataset. We believe that ourdataset can reliably serve as an Arabic multidialectal translation dataset forinformal MT tasks.

OSN-MDAD: Machine Translation Dataset for Arabic Multi-Dialectal Conversations on Online Social Media