Parsing dialectal Arabic remains underexplored, with limited progress over the past two decades. Existing Modern Standard Arabic (MSA) parsers perform poorly on dialectal data, motivating the need for dialect-specific approaches. We revisit this task using modern neural models and present new results on Egyptian and Gulf Arabic dependency parsing. We demonstrate that even small amounts of dialectal training data yield substantial improvements in parsing accuracy. Our contributions include: (1) introducing a new annotated dataset for Gulf Arabic, (2) releasing a state-of-the-art multi-variety Arabic parser, and (3) employing dialect identification as a diagnostic tool to better understand how training data affects parsing performance across dialects and test sets.

We present Palmyra 3.0, a cloud-based, configurable, and user-friendly platform for morphology and syntax annotation through dependency-tree visualization. Palmyra 3.0 implements a robust system that stores data on the cloud. By default, Palmyra 3.0 comes with an Arabic dependency parser that generates highly accurate trees, but it is easily configurable to support dependency parsers in other languages. Palmyra 3.0 provides default configuration files for a number of predefined formalisms, such as UD and CATiB, and a number of user-friendly features to support annotators.