---
title: "DeformAr — paper by Younes (2025) — summary and ideas"
description: "The author presents it as the first Arabic-specific, component-based interpretability tool. 99 references."
authors: ["Ahmed Mustafa Younes"]
year: 2025
venue: "arXiv:2512.00938"
source: "https://arxiv.org/abs/2512.00938"
doi: "10.48550/arxiv.2512.00938"
arxiv: "2512.00938"
licence: "arXiv — non-exclusive license to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/younes-2025-deformar-rethinking-ner-evaluation-through/"
---
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# DeformAr: Rethinking NER Evaluation through Component Analysis and Visual Analytics

**Authors:** Ahmed Mustafa Younes · **Year:** 2025 · **Venue:** arXiv:2512.00938 · **Licence:** [arXiv — non-exclusive license to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2512.00938) · [PDF](https://arxiv.org/pdf/2512.00938) · [DOI 10.48550/arxiv.2512.00938](https://doi.org/10.48550/arxiv.2512.00938) · [arXiv 2512.00938](https://arxiv.org/abs/2512.00938) · [OpenAlex](https://openalex.org/W4416978032)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
This doctoral thesis (University of Sussex) asks why transformer NER systems perform worse on Arabic than on English, even when the Arabic models were pre-trained on more data. Candidate causes include tokenisation, dataset quality and annotation inconsistencies. Earlier work studied these one at a time, which hides how they interact. The author built DeformAr (Debugging and Evaluation Framework for Transformer-based NER Systems). It pairs a data extraction library with an interactive dashboard and splits each language's system into dataset and model components so their interplay can be examined.

The analysis runs in two stages. A cross-component stage applies systematic diagnostic measures to data, model and evaluation subcomponents to establish what differs and how. A behavioural stage then combines interpretability methods, token-level metrics, interactive visualisation and representation-space analysis to explain why. The author presents it as the first Arabic-specific, component-based interpretability tool. It is a reusable resource for analysing models in under-resourced languages and a template for diagnosis that goes beyond a single F1 score.

## Key points
- The case study compares Arabic and English NER with transformer models and is organised around research questions RQ0–RQ5, covering framework design, data, model, evaluation, cross-component analysis and behaviour.
- The data-side diagnostics include:
  - structural analysis,
  - lexical diversity,
  - out-of-vocabulary (OOV) rates,
  - vocabulary overlap across entity tags,
  - token-level behaviour.
- The model-side diagnostics cover loss, prediction confidence and representation space. The evaluation side covers span-level error patterns and the correlation between class support and performance.
- The behavioural stage examines:
  - how fine-tuning changes attention and token embeddings,
  - Arabic-specific anomalies,
  - the effect of tokenisation,
  - misalignment in the annotation scheme.
- The dashboard is organised into three tabs: one for analysis across components, one for examining behaviour, and one for viewing individual instances. The appendix gives classification reports for a variant called DeCoNLL.

## Interesting ideas
- Instead of blaming a single cause for a performance gap, split the pipeline into data, model and evaluation components and measure how they interact, so that each explanation is tied to evidence from more than one component.
- Cluster the model's internal representations and test how well the clusters match the gold labels at three levels: boundaries only, types only, and both combined. This shows which part of the labelling the encoder actually separates.
- Measure how often the same word form occurs under different labels, which gives a cheap data-only estimate of inherent ambiguity before any model is trained.
- Correlate each class's support with its score, which separates classes that fail because they are rare from classes that are hard despite plenty of examples.
- Treat annotation-scheme misalignment as its own error source, distinct from model mistakes, and surface it at the instance level.

## For Mova

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

- **I1** (learn): When LAS differs between treebanks or genres, Mova could report data statistics (OOV, lexical diversity) next to model signals (loss, confidence). That would show whether the gap comes from the data or from the parser.
- **I2** (tree): Mova could cluster gparse encoder states and compare them with UPOS and with deprel separately. Labels the encoder fails to separate would point to where more training data or features are needed.
- **I3** (tokens): Running this per word form across tagger and lemma labels would flag forms that are truly ambiguous. These could get context-sensitive rules rather than lexicon defaults.
- **I4** (learn): Plotting per-deprel scores against their frequency in silver data would show where adding data helps and where repair rules or teacher tiers are a better use of effort.
- **I5** (tree): During UD dialect conversion, Mova could flag tokens where converted gold and parser output disagree in a scheme-patterned way. That keeps conversion artefacts from being counted as parser errors.

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

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## Cite

Cite as: Ahmed Mustafa Younes. 2025. DeformAr: Rethinking NER Evaluation through Component Analysis and Visual Analytics. arXiv:2512.00938.

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

```bibtex
@misc{younes2025deformar,
  title = {{DeformAr: Rethinking NER Evaluation through Component Analysis and Visual Analytics}},
  author = {Ahmed Mustafa Younes},
  year = {2025},
  doi = {10.48550/arxiv.2512.00938},
  eprint = {2512.00938},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2512.00938}
}
```

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## References

99 references, 26 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 24 October 2026*