---
title: "ALT Submission for OSACT Shared Task on Offensive Language"
description: "The ALT team from Qatar Computing Research Institute entered the OSACT4 shared task on Arabic… 12 references, cited by 17 papers here."
authors: ["Sabit Hassan", "Younes Samih", "Hamdy Mubarak", "Ahmed Abdelali", "Ammar Rashed", "Shammur Absar Chowdhury"]
year: 2020
venue: "Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection"
source: "https://aclanthology.org/2020.osact-1.9/"
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/hassan-2020-alt-submission-osact-shared-task/"
---
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# ALT Submission for OSACT Shared Task on Offensive Language Detection

**Authors:** [Sabit Hassan](https://md.research.elamur.ai/free/en/authors/sabit-hassan/), [Younes Samih](https://md.research.elamur.ai/free/en/authors/younes-samih/), [Hamdy Mubarak](https://md.research.elamur.ai/free/en/authors/hamdy-mubarak/), [Ahmed Abdelali](https://md.research.elamur.ai/free/en/authors/ahmed-abdelali/), [Ammar Rashed](https://md.research.elamur.ai/free/en/authors/ammar-rashed/) and [Shammur Absar Chowdhury](https://md.research.elamur.ai/free/en/authors/shammur-absar-chowdhury/) · **Year:** 2020 · **Venue:** [Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection](https://md.research.elamur.ai/free/en/venues/osact/) ([Workshop on Open-Source Arabic Corpora and Processing Tools with Shared Tasks 2020](https://md.research.elamur.ai/free/en/venues/osact/2020/)) · **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/2020.osact-1.9/) · [PDF](https://aclanthology.org/2020.osact-1.9.pdf) · [OpenAlex](https://openalex.org/W3087889451)

**Citations:** 22 citations (OpenAlex, 2026-10-14) · [17](https://md.research.elamur.ai/free/en/papers/hassan-2020-alt-submission-osact-shared-task/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
The ALT team from Qatar Computing Research Institute entered the OSACT4 shared task on Arabic tweets. The task had two parts: flagging offensive language (Subtask A) and flagging hate speech (Subtask B). The organizers supplied 10,000 labelled tweets with a strong class imbalance: about 19% are offensive and only about 5% are hate speech. Ranking used macro-averaged F1. After a fixed four-step cleanup of the tweets, the authors compared many classifiers:

- SVMs on tf-idf character and word n-grams, plus Mazajak Twitter embeddings
- FastText
- a feed-forward network
- CNN and CNN-BiLSTM
- fine-tuned M-BERT

Tuned SVMs beat every neural model when used alone. Because the model families differ, the authors expected their errors to differ too, so they combined them. For Subtask A, a majority vote over two SVMs, CNN-BiLSTM and M-BERT took 1st place with a test F1 of 90.51%. For Subtask B, three SVMs labelled a tweet as hate speech if any single one said so, which placed 4th with an F1 of 80.63%. The paper shows that cheap, diverse ensembles work well for low-resource, noisy, skewed classification.

## Key points
- **Data:** 10,000 Arabic tweets, split 7,000 train / 1,000 dev / 2,000 test. All hate speech counts as offensive by definition.
- **Preprocessing:** the authors applied four steps in order:
  1. drop words containing non-Arabic characters
  2. strip diacritics
  3. remove punctuation
  4. collapse repeated letters into one
- **Best single model on dev for Subtask A:** an SVM on Mazajak SG-250 embeddings plus char [1-5] and word [1-3] grams, with F1 90.4. By comparison, CNN-BiLSTM reached 87.8, M-BERT 83.3 and FastText 79.4.
- **Domain mismatch:** the authors attribute M-BERT's weaker score to its formal Wikipedia pretraining, which differs from informal Twitter text.
- **Error analysis for Subtask A:** misclassified tweets fell into three groups: missing context, out-of-vocabulary words, and ambiguous words (for example, a word read as offensive in a harmless sentence).
- **Hate speech remains hard:** the dev confusion matrix for Subtask B shows 16 of 42 hate-speech tweets missed. Data augmentation is planned as future work.

## Interesting ideas
- Pick ensemble members for how different their errors are, not only for top scores; mixing SVMs with neural models beat every single system.
- For a very rare class, switch from majority voting to an "any member says yes" rule to trade precision for recall; this won on dev for hate speech.
- A character-level CNN learned jointly with word embeddings covers words missing from the pretrained vocabulary and captures morphology.
- Domain fit of pretraining mattered more than model power: Twitter-trained Mazajak embeddings beat Wikipedia-trained M-BERT.
- Sorting a sample of errors into named causes (context, out-of-vocabulary, ambiguity) turns failures into a research agenda.

## For Mova

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

- **I1** (learn): when agreeing silver data across witness parsers, prefer witnesses built on different principles (perceptron vs gparse), so that agreement is more informative.
- **I2** (frames): rare pragmatic labels such as irony or implicature could use a union vote among detectors as a high-recall candidate stage, followed by an explainable confirming check.
- **I3** (tokens): character-based fallback features could help guess lemma and POS for unseen or misspelled informal English words, before the tagger gives up.
- **I4** (tree): when training gparse for fables, school texts or forum text, weigh treebank and pretraining domain match explicitly, and report scores per domain.
- **I5** (learn): tag evaluation failures with a fixed cause taxonomy, so that self-induced repair rules can target one cause class at a time and be checked against negative controls.

*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

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## Cite

Cite as: Sabit Hassan, Younes Samih, Hamdy Mubarak, Ahmed Abdelali, Ammar Rashed and Shammur Absar Chowdhury. 2020. ALT Submission for OSACT Shared Task on Offensive Language Detection. Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection.

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

```bibtex
@inproceedings{hassan2020alt,
  title = {{ALT Submission for OSACT Shared Task on Offensive Language Detection}},
  author = {Sabit Hassan, Younes Samih, Hamdy Mubarak, Ahmed Abdelali, Ammar Rashed and Shammur Absar Chowdhury},
  booktitle = {Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection},
  year = {2020},
  url = {https://aclanthology.org/2020.osact-1.9/}
}
```

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## References

12 references, 7 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Abu Farha, I. and Magdy, W. (2019). [Mazajak: An online Arabic sentiment analyser](https://md.research.elamur.ai/free/en/papers/farha-2019-mazajak-online-arabic-sentiment-analyser/). In Proceedings of the Fourth Arabic Natural Language Processing Workshop, pages 192–198, Florence, Italy, August. Association for Computational Linguistics.
2. Agrawal, S. and Awekar, A. (2018). Deep learning for detecting cyberbullying across multiple social media platforms. CoRR, abs/1801.06482. [↗](https://arxiv.org/abs/1801.06482)
3. Alakrot, A., Murray, L., and Nikolov, N. S. (2018). Towards accurate detection of offensive language in online communication in arabic. Procedia Computer Science, 142:315 – 320. Arabic Computational Linguistics. [find](https://scholar.google.com/scholar?q=Alakrot%2C+A%2E%2C+Murray%2C+L%2E%2C+and+Nikolov%2C+N%2E+S%2E+%282018%29%2E+Towards+accurate+detection+of+offensive+language+in+online+communication+in+arabic%2E+Procedia+Computer+Science%2C+142%3A315+%E2%80%93+320%2E+Arabic+Computational+L)
4. Albadi, N., Kurdi, M., and Mishra, S. (2018). Are they our brothers? analysis and detection of religious hate speech in the arabic twittersphere. In 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pages 69–76, Aug. [find](https://scholar.google.com/scholar?q=Albadi%2C+N%2E%2C+Kurdi%2C+M%2E%2C+and+Mishra%2C+S%2E+%282018%29%2E+Are+they+our+brothers%3F+analysis+and+detection+of+religious+hate+speech+in+the+arabic+twittersphere%2E+In+2018+IEEE%2FACM+International+Conference+on+Advances+)
5. Davidson, T., Warmsley, D., Macy, M. W., and Weber, I. (2017). [Automated hate speech detection and the problem of offensive language](https://md.research.elamur.ai/free/en/papers/davidson-2017-automated-hate-speech-detection-problem/). CoRR, abs/1703.04009. [↗](https://arxiv.org/abs/1703.04009)
6. Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2019). [BERT: Pre-training of deep bidirectional transformers for language understanding](https://md.research.elamur.ai/free/en/papers/devlin-2019-bert-pre-training-deep-bidirectional/). In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171–4186, Minneapolis, Minnesota, June. Association for Computational Linguistics. [↗](https://doi.org/10.18653/v1/n19-1423)
7. Djuric, N., Zhou, J., Morris, R., Grbovic, M., Radosavljevic, V., and Bhamidipati, N. (2015). Hate speech detection with comment embeddings. In Proceedings of the 24th International Conference on World Wide Web, WWW ’15 Companion, page 29–30, New York, NY, USA. Association for Computing Machinery. [find](https://scholar.google.com/scholar?q=Djuric%2C+N%2E%2C+Zhou%2C+J%2E%2C+Morris%2C+R%2E%2C+Grbovic%2C+M%2E%2C+Radosavljevic%2C+V%2E%2C+and+Bhamidipati%2C+N%2E+%282015%29%2E+Hate+speech+detection+with+comment+embeddings%2E+In+Proceedings+of+the+24th+International+Conference+on+Worl)
8. Joulin, A., Grave, E., Bojanowski, P., Douze, M., Jégou, H., and Mikolov, T. (2016). [Fasttext.zip: Compressing text classification models](https://md.research.elamur.ai/free/en/papers/joulin-2016-fasttext-zip-compressing-text-classification/). CoRR, abs/1612.03651. [↗](https://arxiv.org/abs/1612.03651)
9. Kim, Y., Jernite, Y., Sontag, D. A., and Rush, A. M. (2015). [Character-aware neural language models](https://md.research.elamur.ai/free/en/papers/kim-2015-character-aware-neural-language-models/). CoRR, abs/1508.06615. [↗](https://arxiv.org/abs/1508.06615)
10. Mubarak, H. and Darwish, K. (2019). Arabic offensive language classification on twitter. In International Conference on Social Informatics, pages 269–276. Springer. [find](https://scholar.google.com/scholar?q=Mubarak%2C+H%2E+and+Darwish%2C+K%2E+%282019%29%2E+Arabic+offensive+language+classification+on+twitter%2E+In+International+Conference+on+Social+Informatics%2C+pages+269%E2%80%93276%2E+Springer%2E)
11. Mubarak, H., Darwish, K., and Magdy, W. (2017). [Abusive language detection on Arabic social media](https://md.research.elamur.ai/free/en/papers/mubarak-2017-abusive-language-detection-arabic-social/). In Proceedings of the First Workshop on Abusive Language Online, pages 52–56, Vancouver, BC, Canada, August. Association for Computational Linguistics.
12. Mubarak, H., Darwish, K., Magdy, W., Elsayed, T., and Al-Khalifa, H. (2020). [Overview of osact4 arabic offensive language detection shared task](https://md.research.elamur.ai/free/en/papers/mubarak-2020-overview-osact4-arabic-offensive-language/). 4.

*Updated 1 November 2026*