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Arabic Morphosyntactic Tagging and Dependency Parsing with Large Language Models

What it is about

The authors test whether instruction-tuned large language models can produce explicit Arabic grammatical analysis: 14 morphological features per token (part of speech, aspect, mood, voice, person, gender, number, case, state, four proclitics and one enclitic), and labelled CATiB dependency trees. They run three settings: pre-tokenised input, raw text, and a cascade in which predicted morphology is given to the parser. They compare zero-shot prompting with retrieval of demonstrations by character-level similarity (chrF++) or by embedding similarity, at 0 to 10 shots. Open-weight models struggle without demonstrations and improve with retrieved ones; the strongest proprietary models approach the supervised tagger and the CamelParser 2.0, but need a large annotated pool for retrieval and much compute. The abstract's claim is the one used here; the card does not take the paper's numbers.

Ideas

For Mova

I1 goes into gparse and Syntax: the clause and the sentence, measured on MACULA. I2 is for the teacher pipeline described in docs/ (the teacher labels, the SLM decides), and is recorded in tools/hbo. I3 is a test for tools/hbo recorded in 13. Biblical Hebrew in Mova: the tools, with the clitic slots in Pronouns, particles and prepositions.

Topics: Dependency parsing

Cite

The paper itself, not this card. BibTeX from the library's record of the work. arXiv 2603.16718

@misc{adel2026arabic,
  title = {{Arabic Morphosyntactic Tagging and Dependency Parsing with Large Language Models}},
  author = {Adel, Mohamed and Alhafni, Bashar and Habash, Nizar},
  year = {2026},
  eprint = {2603.16718},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2603.16718}
}

References

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