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Parsing Arabic Dialects Revisited: New Benchmarks, Models, and Insights

What it is about

The paper revisits dependency parsing of dialectal Arabic, a task with little progress since the mid-2000s. The authors build a Gulf Arabic treebank of over 25K tokens in the CATiB style, from conversational internet-novel text, and train one biaffine parser (BERT-based encoder, CAMeLBERT-MIX) on MSA, Egyptian and Gulf data, varying only the data mix. A dialect identifier labels each test sentence with its predicted variety, so results are read per subset as well as per file. On the test sets the new parser stays close to CamelParser2.0 on MSA (LAS 87.2 against 87.5) and improves Egyptian from 73.3 to 84.2 and Gulf from 72.7 to 80.3 LAS. Gains on root identification are high in precision and low in recall for the fragmentary Gulf data.

Ideas

For Mova

I1 goes into the evaluation of tools/langseg output. I2 is a training plan for gparse over languages/hbo/ and languages/arc/ trees. I3 is an acceptance check for treebanks/. I4 is a column in every gparse report. The Gulf treebank is non-commercial: it may serve as a test set for measurements, never as training data in what is built.

Topics: Treebanks

Cite

The paper itself, not this card. BibTeX from the library's record of the work. DOI 10.63317/3eyeu3k726ab

@inproceedings{elshabrawy2026parsing,
  title = {{Parsing Arabic Dialects Revisited: New Benchmarks, Models, and Insights}},
  author = {Elshabrawy, Ahmed Farouk Zakaria and Inoue, Go and AbuOdeh, Muhammed and Habash, Nizar},
  booktitle = {The 7th Workshop on Open-Source Arabic Corpora and Processing Tools (OSACT7) with 5 Shared Tasks},
  year = {2026},
  doi = {10.63317/3eyeu3k726ab},
  url = {https://doi.org/10.63317/3eyeu3k726ab}
}

References

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