---
title: "GOF at Arabic Hate Speech 2022 — paper by Mostafa et al. (2022)"
description: "The paper addresses offensive-language detection in Arabic tweets for the OSACT5 shared task. 55 references, cited by 2 papers here."
authors: ["Ali Mostafa", "Omar Mohamed", "Ali Ashraf"]
year: 2022
venue: "Proceedinsg of the 5th Workshop on Open-Source Arabic Corpora and Processing Tools with Shared Tasks on Qur'an QA and Fine-Grained Hate Speech Detection"
source: "https://aclanthology.org/2022.osact-1.21/"
licence: "CC BY-NC 4.0 (ELRA, LREC proceedings)"
licence_url: "https://creativecommons.org/licenses/by-nc/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/mostafa-2022-gof-arabic-hate-speech-2022/"
---
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# GOF at Arabic Hate Speech 2022: Breaking The Loss Function Convention For Data-Imbalanced Arabic Offensive Text Detection

**Authors:** [Ali Mostafa](https://md.research.elamur.ai/free/en/authors/ali-mostafa/), [Omar Mohamed](https://md.research.elamur.ai/free/en/authors/omar-mohamed/) and [Ali Ashraf](https://md.research.elamur.ai/free/en/authors/ali-ashraf/) · **Year:** 2022 · **Venue:** [Proceedinsg of the 5th Workshop on Open-Source Arabic Corpora and Processing Tools with Shared Tasks on Qur'an QA and Fine-Grained Hate Speech Detection](https://md.research.elamur.ai/free/en/venues/osact/) ([Workshop on Open-Source Arabic Corpora and Processing Tools with Shared Tasks 2022](https://md.research.elamur.ai/free/en/venues/osact/2022/)) · **Publisher:** [European Language Resources Association (ELRA)](https://md.research.elamur.ai/free/en/publishers/european-language-resources-association-elra/) · **Licence:** [CC BY-NC 4.0 (ELRA, LREC proceedings)](https://creativecommons.org/licenses/by-nc/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2022.osact-1.21/) · [PDF](https://aclanthology.org/2022.osact-1.21.pdf)

**Citations:** [2](https://md.research.elamur.ai/free/en/papers/mostafa-2022-gof-arabic-hate-speech-2022/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
The paper addresses offensive-language detection in Arabic tweets for the OSACT5 shared task. The core difficulty is a long-tailed label distribution. In the roughly 13k-tweet dataset of Mubarak et al. (2022), only about 35% of tweets are offensive and 11% are hate speech, and some fine-grained categories are far rarer.

The common remedy is cross-entropy with class weights set inversely to class frequency. Instead of relying on it, the authors test a set of imbalance-oriented objectives while fine-tuning several Arabic pretrained encoders, including MARBERT, MARBERTV2, QARiB and the AraBERT Twitter models. The objectives are focal, Dice, Tversky, focal Tversky and VS loss, plus label-smoothing variants. Tweets are first cleaned: URLs, mentions and emails become Arabic placeholder words, and diacritics, digits, punctuation and non-Arabic characters are removed before normalization.

Changing the loss raised macro F1 by about 0.5–2 points over weighted cross-entropy. The final ensemble of MARBERT, MARBERTV2 and QARiB reaches about 87% macro F1 on development and 85.17% on test. The study shows that the training objective is a cheap, data-free lever for skewed classification problems.

## Key points
- Data: the OSACT-2022 corpus of about 13k emoji-bearing Arabic tweets, split 70/10/20 into train, dev and test, with labels for offensive, hate speech and fine-grained hate speech.
- Losses compared:
  - Weighted CE, alone and with label smoothing
  - Focal loss, alone and with label smoothing
  - Dice, Tversky, focal Tversky and VS loss
- The best loss differed by model: VS loss for MARBERT (85.6), focal loss with label smoothing for MARBERTV2 (85.2), and Dice loss for QARiB (85.7).
- The best single system on dev was LightGBM trained on QARiB embeddings (85.798 macro F1). The MARBERTV2+MARBERT+QARiB ensemble scored 87.044 on dev (the abstract reports 86.97%) and 85.17% on test.
- Evaluation used macro precision, recall and F1, since accuracy is misleading under heavy imbalance. Captum's Integrated Gradients was used to show which tokens and emojis drove each prediction.

## Interesting ideas
- Treat the loss function as a tunable knob for rare classes, trying focal, Dice or Tversky objectives instead of defaulting to frequency-weighted cross-entropy.
- Run the same set of losses across several backbones, because the winning objective changed from model to model rather than being universal.
- Report macro-averaged F1 rather than accuracy whenever classes are skewed, so poor minority-class performance cannot hide behind majority-class hits.
- Replace URLs, mentions and emails with fixed placeholder words instead of deleting them, so the model keeps the signal that such an element was present.
- Use Integrated Gradients attributions to show which tokens pushed a prediction toward each class, making a neural decision inspectable per example.

## For Mova

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

- **I1** (tree): gparse could train with focal or Tversky loss so rare UD relations such as vocative, reparandum or dislocated are not swamped by frequent ones like nsubj or det.
- **I2** (learn): when retraining the perceptron and transformer parsers, sweep losses per architecture instead of copying one parser's best setting to the other.
- **I3** (frames): speech-act and irony detectors, where ironic or directive utterances are rare, should be scored with per-class and macro metrics in evaluation.
- **I4** (tokens): the en tokenizer could map web artifacts to typed placeholder tokens with a stable lemma and POS, keeping UD trees well-formed around them.
- **I5** (frames): any neural component in prag, such as an irony classifier, could attach token attributions to its output, supporting the requirement that every step be explainable.

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

📚 Shelves (2): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 16,667 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 16,194 works

🧭 Topics (3): [😊 Sentiment & emotion](https://md.research.elamur.ai/free/en/topics/sentiment-emotion/) · 972 works · [Semitic languages](https://md.research.elamur.ai/free/en/topics/semitic-languages/) · 2,908 works · [🛡️ Bias, fairness & safety](https://md.research.elamur.ai/free/en/topics/bias-fairness-safety/) · 517 works

## Cite

Cite as: Ali Mostafa, Omar Mohamed and Ali Ashraf. 2022. GOF at Arabic Hate Speech 2022: Breaking The Loss Function Convention For Data-Imbalanced Arabic Offensive Text Detection. Proceedinsg of the 5th Workshop on Open-Source Arabic Corpora and Processing Tools with Shared Tasks on Qur'an QA and Fine-Grained Hate Speech Detection.

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

```bibtex
@inproceedings{mostafa2022gof,
  title = {{GOF at Arabic Hate Speech 2022: Breaking The Loss Function Convention For Data-Imbalanced Arabic Offensive Text Detection}},
  author = {Ali Mostafa, Omar Mohamed and Ali Ashraf},
  booktitle = {Proceedinsg of the 5th Workshop on Open-Source Arabic Corpora and Processing Tools with Shared Tasks on Qur'an QA and Fine-Grained Hate Speech Detection},
  year = {2022},
  url = {https://aclanthology.org/2022.osact-1.21/}
}
```

- [← previous Fine-Tuning Approach for Arabic Offensive Language Detection System: BERT-Based Model](https://md.research.elamur.ai/free/en/papers/husain-2022-fine-tuning-approach-arabic-offensive/)
- [next → Handling Stress in Finite-State Morphological Analyzers for Ancient Greek and Ancient Hebrew](https://md.research.elamur.ai/free/en/papers/swanson-2022-handling-stress-finite-state-morphological/)
- [↑ back to papers of 2022](https://md.research.elamur.ai/free/en/papers/2022/page/8/)

## References

55 references, 32 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*