How many Syrians have returned? A ceasefire between the Syrian army and the Kurdish-led Syrian Democratic Forces (SDF) appears to be largely holding, even as the two sides have accused each other of violating its terms. The army on Sunday said the SDF launched multiple drone attacks in the Aleppo countryside, while the United States-trained Kurdish forces on Monday accused the army of targeting a Kurdish-majority city near the Turkish border. An initial four-day ceasefire between the Syrian army and the SDF was extended by 15 days soon after it expired on Saturday night. The official Syrian Arab News Agency (SANA) reported that the SDF launched more than 25 explosive drones on the army positions in the Aleppo countryside on Sunday, breaching the newly extended ceasefire.

Could Ukraine hold a presidential election right now? Will Europe use frozen Russian assets to fund war? How can Ukraine rebuild China ties? 'Ukraine is running out of men, money and time' Video released by the Ukrainian military showed soldiers shooting down small Russian drones with their rifles near the small Donetsk village of Kostiantynivka. Russian forces have made steady yet costly gains in the region, claiming on Monday to have captured nearby Dibrova.

How dangerous is the situation in the West Bank? What does survival look like inside Gaza City? The Gaza-bound Global Sumud Flotilla (GSF) says a drone has struck its main ship in the Tunisian port of Sidi Bou Said, causing a fire, but that all its passengers and crew were safe. A spokesman for the GSF blamed Israel for the incident, which occurred late on Monday, but the Tunisian National Guard said reports of a drone attack were "completely unfounded". The GSF, however, insisted the incident was a drone attack and said it would provide more details on Tuesday morning.

The rich linguistic landscape of the Arab world is characterized by a significant gap between Modern Standard Arabic (MSA), the language of formal communication, and the diverse regional dialects used in everyday life. This diglossia presents a formidable challenge for natural language processing, particularly machine translation. This paper introduces \textbf{SHAMI-MT}, a bidirectional machine translation system specifically engineered to bridge the communication gap between MSA and the Syrian dialect. We present two specialized models, one for MSA-to-Shami and another for Shami-to-MSA translation, both built upon the state-of-the-art AraT5v2-base-1024 architecture. The models were fine-tuned on the comprehensive Nabra dataset and rigorously evaluated on unseen data from the MADAR corpus. Our MSA-to-Shami model achieved an outstanding average quality score of \textbf{4.01 out of 5.0} when judged by OPENAI model GPT-4.1, demonstrating its ability to produce translations that are not only accurate but also dialectally authentic. This work provides a crucial, high-fidelity tool for a previously underserved language pair, advancing the field of dialectal Arabic translation and offering significant applications in content localization, cultural heritage, and intercultural communication.

This paper presents Nabra, a corpora of Syrian Arabic dialects with morphological annotations. A team of Syrian natives collected more than 6K sentences containing about 60K words from several sources including social media posts, scripts of movies and series, lyrics of songs and local proverbs to build Nabra. Nabra covers several local Syrian dialects including those of Aleppo, Damascus, Deir-ezzur, Hama, Homs, Huran, Latakia, Mardin, Raqqah, and Suwayda. A team of nine annotators annotated the 60K tokens with full morphological annotations across sentence contexts. We trained the annotators to follow methodological annotation guidelines to ensure unique morpheme annotations, and normalized the annotations. F1 and kappa agreement scores ranged between 74% and 98% across features, showing the excellent quality of Nabra annotations. Our corpora are open-source and publicly available as part of the Currasat portal https://sina.birzeit.edu/currasat.

Recent work has utilised knowledge-aware approaches to natural language understanding, question answering, recommendation systems, and other tasks. These approaches rely on well-constructed and large-scale knowledge graphs that can be useful for many downstream applications and empower knowledge-aware models with commonsense reasoning. Such knowledge graphs are constructed through knowledge acquisition tasks such as relation extraction and knowledge graph completion. This work seeks to utilise and build on the growing body of work that uses findings from the field of natural language processing (NLP) to extract knowledge from text and build knowledge graphs. The focus of this research project is on how we can use transformer-based approaches to extract and contextualise event information, matching it to existing ontologies, to build a comprehensive knowledge of graph-based event representations. Specifically, sub-event extraction is used as a way of creating sub-event-aware event representations. These event representations are then further enriched through fine-grained location extraction and contextualised through the alignment of historically relevant quotes.

Training on only perfect Standard English corpora predisposes pre-trained neural networks to discriminate against minorities from non-standard linguistic backgrounds (e.g., African American Vernacular English, Colloquial Singapore English, etc.). We perturb the inflectional morphology of words to craft plausible and semantically similar adversarial examples that expose these biases in popular NLP models, e.g., BERT and Transformer, and show that adversarially fine-tuning them for a single epoch significantly improves robustness without sacrificing performance on clean data.

Gen. Igor Konashenkov, says in just one month air defense assets have downed 45 drones targeting the Hemeimeem air base. The base in the province of Latakia serves as the main hub for Russian operations in Syria.

"More than a dozen armed drones descended from an unknown location onto Russia's vast Hmeimim air base in northwestern Latakia province, the headquarters of Russia's military operations in Syria, and on the nearby Russian naval base at Tartus," The Washington Post reported. "Russia said that it shot down seven of the 13 drones and used electronic countermeasures to safely bring down the other six." And these drones appeared substantially less sophisticated and maneuverable than a DJI Phantom 4, the leading consumer drone. The National Academy notes that most of the counterstrategies that the Army has developed are "based on jamming radio frequency and GPS signals." The thinking was: Drones needed those information flows to navigate effectively.

President Vladimir V. Putin of Russia, in a surprise visit to that air base on Dec. 11, declared that combat operations were winding down and that the Russian military would stage a "significant withdrawal." It was at least the second time he had made such an announcement since March 2016. Mr. Putin faces a presidential election this March, and although he is expected to win easily, polls indicate that Russians are increasingly disgruntled about the country's military presence in Syria. Please verify you're not a robot by clicking the box. You must select a newsletter to subscribe to.