CamelParser2.0: A State-of-the-Art Dependency Parser for Arabic

What it is about

An open-source Arabic parsing pipeline for both the CATiB and the UD formalisms. Raw text is normalised and stripped of diacritics, then a morphological disambiguator from CAMeL Tools (a BERT model or a lighter MLE one) gives tokens, lemmas, POS and features, including position-marked proclitics and enclitics and a token_type feature (base word or clitic slot). A biaffine parser (SuPar) over BERT embeddings (last four layers summed, word pieces mean-pooled) then gives the tree, and the morphology is merged into the CoNLL-U output afterwards. Training data are PATB-CATiB and the multi-genre Camel Treebank (classical and modern texts), and PADT and NUDAR for UD; non-projective trees are dropped from training only. The paper reports every result under gold and under predicted tokenization: predicted word-level tokenization on the dev sets averages 96.8%, from 99.8% on WikiNews down to 91.3% on classical Odes, and parsing drops accordingly. Training on all CATiB data together is best on average; a classical-Arabic BERT helps on classical texts and hurts on modern news (for example +2.5 LAS on Odes, −3.5 on WikiNews). On UD (PADT, NUDAR) the system beats UDPipe 2 with the same tokens.

Ideas

• I1. Report every parse twice, with gold and with predicted segmentation, and list the segmentation accuracy per genre next to it. The 99.8% vs 91.3% spread shows that the segmenter, trained on one genre, is the first thing that fails on another; for tools/hbo the genres are prose, poetry and Mishnaic text.
• I2. A token_type feature (base word vs clitic in a named slot) on every syntactic word, as here: it lets the parser and the rules know that a word was a prefix of a written token, and it can be checked against the segmenter tables.
• I3. Train on all genres together but measure per genre, and keep the genre-specific embedding/lexicon choice as a switch: the period-specific model helps its own period and hurts the other, so a single "best" setting would hide the trade-off.
• I4. Exclude non-projective trees from training but not from evaluation, and report how many were excluded; a simple, explicit rule for gparse training on PTNK and the MACULA-derived trees.

For Mova

I1 and I2 apply to tools/hbo (segmenter output and its evaluation, recorded in 13. Biblical Hebrew in Mova: the tools); I3 to the genre splits of tools/hbo; I4 to gparse training. The neural parts are not taken; the reporting and data rules are.

Topics: Dependency parsing

Cite

The paper itself, not this card. BibTeX from the library's record of the work. DOI 10.18653/v1/2023.arabicnlp-1.15

@inproceedings{elshabrawy2023camelparser2,
  title = {{CamelParser2.0: A State-of-the-Art Dependency Parser for Arabic}},
  author = {Elshabrawy, Ahmed and AbuOdeh, Muhammed and Inoue, Go and Habash, Nizar},
  booktitle = {Proceedings of ArabicNLP 2023},
  year = {2023},
  doi = {10.18653/v1/2023.arabicnlp-1.15},
  url = {https://doi.org/10.18653/v1/2023.arabicnlp-1.15}
}

References

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