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Weakly and Semi-Supervised Learning for Arabic Text Classification using Monodialectal Language Models

Paper · 2022 · Reem AlYami and Rabah Al-Zaidy

Summary

The paper looks at text classification for Arabic dialects. Arabic is widely spoken, but labeled data for individual dialects is scarce. Raw dialectal text is plentiful online, yet it is rarely tagged by dialect. The authors build a Twitter collection pipeline that pairs country geo-coordinates (a centre point and radius) with trending keywords harvested daily. Because the keywords change, the pipeline avoids the topical narrowness of fixed seed words. Two resources come out of it: a dictionary collection called the Arabic Dialect Dictionary dataset (ADD), and a corpus of short posts named ADST (Arabic Dialect Short Text). From the dictionaries they build an automatic labeling function, which includes one hand-crafted rule. They also pretrain seven monodialectal RoBERTa models, called AraRoBERTa. Dialect identification is then tested under full supervision, weak supervision (dictionary-labeled data) and an iterative semi-supervised scheme. Most supervised AraRoBERTa models beat larger contextual baselines, even though they were pretrained on far less data. The semi-supervised variant also tends to improve on its seed-only start. The work matters because the recipe does not depend on a particular language or task, so it could be reused for other under-resourced language varieties.

Key points

  • The seven dialects covered are SA, EG, KU, OM, LB, JO and DZ, each with its own dictionary and its own AraRoBERTa model (e.g. AraRoBERTa-SA). The models are released on Hugging Face (reemalyami).
  • Tweets were collected with the Tweepy API. Each country is defined by a centre point and a radius, and the keywords come from Twitter's daily trends for that country.
  • Semi-supervised setup:
    • Iteration 0 is supervised training on 2,800 labeled samples.
    • In iterations 1–2, pseudo-labeled data from a 16K pool is added. For SA, the training set grows to about 30K.
    • F-1 improves for some dialects (SA 0.818→0.834, DZ 0.859→0.873) but drops for others (OM 0.84→0.784).
  • Weakly supervised models score high on dictionary-labeled validation data (0.88–0.97 F-1) but much lower on the gold test set. The drop ranges from −8.8% (SA) to −51.1% (OM).
  • Error analysis:
    • AraRoBERTa-SA and AraRoBERTa-LB over-accept other dialects, with false-positive rates of about 11.4–11.6% against about 4% false negatives.
    • Many errors come from border regions that share vocabulary, which shows a weakness of defining dialects by country.

Interesting ideas

  • I1. Comparing scores on automatically labeled validation data with scores on a small gold test set exposes how noisy the weak labels are; a large gap marks the label source, not the model, as the problem.
  • I2. In self-training, the authors log how much of the unlabeled pool is still unassigned at each iteration, and they show that extra rounds can hurt as well as help.
  • I3. Refreshing collection keywords daily from trends, instead of using fixed seed words, keeps a corpus from collapsing onto a few topics.
  • I4. Small models pretrained on one narrow variety can beat larger general models on that variety.
  • I5. The error analysis separates mistakes caused by model training data from mistakes caused by how the label categories are drawn, such as country borders cutting through a shared dialect.

For Mova

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

  • I1 (learn): Score every silver-data slice from agreeing witness parsers against a small gold set, and treat a big silver-vs-gold gap as a signal to repair or drop that slice before training.
  • I2 (learn): When LLM-teacher or self-play data is fed back iteratively, track pool consumption and per-round gold scores, and stop early instead of assuming more rounds are always better.
  • I3 (learn): Sample raw text for silver treebanks from rotating, time-varying sources so that the parser does not overfit to the vocabulary and constructions of a few domains.
  • I4 (tree): Consider pretraining or adapting gparse on a focused corpus of the target genre (e.g. tales and school texts) rather than relying only on bigger generic encoders.
  • I5 (frames): When frame or speech-act errors cluster, check whether the category boundaries themselves are fuzzy before blaming the classifier, and record such overlaps as explainable ambiguity.

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

🎓King Fahd University of Petroleum and MineralsDhahran · Saudi Arabia52 works · 4,666 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 3,320 worksSemitic languages · 2,908 works

Cite

Cite as Reem AlYami and Rabah Al-Zaidy. 2022. Weakly and Semi-Supervised Learning for Arabic Text Classification using Monodialectal Language Models. 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. DOI 10.18653/v1/2022.wanlp-1.24

@inproceedings{alyami2022weakly,
  title = {{Weakly and Semi-Supervised Learning for Arabic Text Classification using Monodialectal Language Models}},
  author = {Reem AlYami and Rabah Al-Zaidy},
  booktitle = {Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP)},
  year = {2022},
  doi = {10.18653/v1/2022.wanlp-1.24},
  url = {https://doi.org/10.18653/v1/2022.wanlp-1.24}
}

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