---
title: "Towards Learning Arabic Morphophonology — Khalifa 2022"
description: "Inflection systems often fail on the sound changes that happen when… 82% accuracy on EVAL. 32 references, cited by 4 papers here."
authors: ["Salam Khalifa", "Jordan Kodner", "Owen Rambow"]
year: 2022
venue: "Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP)"
source: "https://aclanthology.org/2022.wanlp-1.27/"
doi: "10.18653/v1/2022.wanlp-1.27"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/khalifa-2022-learning-arabic-morphophonology/"
---
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# Towards Learning Arabic Morphophonology

**Authors:** [Salam Khalifa](https://md.research.elamur.ai/free/en/authors/salam-khalifa/), [Jordan Kodner](https://md.research.elamur.ai/free/en/authors/jordan-kodner/) and [Owen Rambow](https://md.research.elamur.ai/free/en/authors/owen-rambow/) · **Year:** 2022 · **Venue:** [Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP)](https://md.research.elamur.ai/free/en/venues/wanlp/) ([WANLP 2022](https://md.research.elamur.ai/free/en/venues/wanlp/2022/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2022.wanlp-1.27/) · [PDF](https://aclanthology.org/2022.wanlp-1.27.pdf) · [DOI 10.18653/v1/2022.wanlp-1.27](https://doi.org/10.18653/v1/2022.wanlp-1.27) · [OpenAlex](https://openalex.org/W4385572974)

**Citations:** 3 citations (OpenAlex, 2026-10-14) · [4](https://md.research.elamur.ai/free/en/papers/khalifa-2022-learning-arabic-morphophonology/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Inflection systems often fail on the sound changes that happen when morphemes combine. Arabic is a hard case: its morphology mixes templates with affixation, and most of its dialects have few resources. The authors induce explicit, human-readable morphophonological rules for Cairene Egyptian Arabic (EGY), with the longer-term aim of reusing such rules for related dialects.

No suitable dataset existed, so they built one. Surface forms come from the ECAL pronunciation lexicon. Segmented underlying forms are derived from CALIMA EGY analyses, which CamelTools selects by matching orthography, lemma, POS and features.

Rules are extracted in four steps:
- align each (UR, SF) pair with Levenshtein edits;
- trim the context to a narrow window around the changes;
- replace stem consonants with a generic C;
- at prediction time, pick the longest and most specific matching left side, using frequency to break ties.

The rules regenerate surface forms with about 82% accuracy on EVAL and roughly 69% on types never seen in training. The most frequent rules correspond to processes already described in grammars of Egyptian Arabic. This suggests an interpretable alternative to neural inflection models.

## Key points
- The dataset pairs ECAL surface forms (SF) with underlying forms (UR) built from CALIMA EGY segmentations. Segmentation comes from the Buckwalter POS tag, and boundaries are marked with `-` (prefix), `=` (suffix) and `#` (word). Only open-class words (verbs, nouns, adjectives) are kept.
- Split sizes in types are TRAIN 13,170, DEV 5,180 and EVAL 6,974. Overlap-free subsets were also created: DEV-OOV has 2,189 types and EVAL-OOV has 2,271.
- Extraction produced 4,661 rules, about 35.4% of the TRAIN size. Each rule rewrites all changes in one step, and unchanged words map to a "copy" rule.
- Accuracy results:

  | Split | All types | OOV types |
  |---|---|---|
  | TRAIN | 90.1% | – |
  | DEV | 80.5% | 69.4% |
  | EVAL | 82.3% | 68.9% |

- The error analysis covered the 31% of DEV-OOV forms that were produced incorrectly:
  - 32% of these errors had a correct rule already in the inventory, so the selection heuristic was at fault;
  - 68% needed a rule that had never been seen;
  - in a sample of 100 of those unseen rules, every phenomenon was already covered by existing rules, and only the surrounding context was new.

## Interesting ideas
- Create supervision for a task with no annotated corpus by automatically joining two independent resources (a pronunciation lexicon and a morphological analyzer), choosing the best-matching analysis on several fields at once.
- Derive rules from character edit alignments, then generalize them by keeping only a small window around the change and abstracting stem consonants to a placeholder, while vowels and affixes stay explicit.
- Let one rule encode every change of a word at once, in two-level style, so the system never has to discover rule ordering.
- Report accuracy on full running-text splits and also on overlap-removed OOV splits, so memorization and generalization are measured separately.
- Sort errors into "the right rule existed but selection chose wrongly" versus "the needed rule was never learned" to decide whether to fix the ranking or the rule induction.

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (learn): silver morphology pairs could come from aligning treebank forms with analyzer or WordNet lemma entries and keeping only entries where all fields agree, giving cheap training data.
- **I2** (tokens): English spelling changes at morpheme joins (consonant doubling, y→i, e-deletion) could be learned as readable rewrite rules from lemma+affix → form pairs, replacing hand-written exceptions.
- **I3** (back): when generating surface words from lemma plus features, single whole-change rules keep each output traceable to exactly one rule.
- **I4** (learn): every lemmatizer or repair-rule evaluation could include an unseen-type subset as a built-in control against inflated scores.
- **I5** (learn): self-induced repair rules could be audited the same way, showing whether failures come from rule choice or from missing coverage.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Stony Brook University](https://md.research.elamur.ai/free/en/universities/stony-brook-university/) — University, Stony Brook, United States · 43 works · 2,571 citations

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## Cite

Cite as: Salam Khalifa, Jordan Kodner and Owen Rambow. 2022. Towards Learning Arabic Morphophonology. Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP).

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

```bibtex
@inproceedings{khalifa2022learning,
  title = {{Towards Learning Arabic Morphophonology}},
  author = {Salam Khalifa, Jordan Kodner and Owen Rambow},
  booktitle = {Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP)},
  year = {2022},
  doi = {10.18653/v1/2022.wanlp-1.27},
  url = {https://doi.org/10.18653/v1/2022.wanlp-1.27}
}
```

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## References

32 references, 12 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 1 November 2026*