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A Multi-Task Benchmark for Abusive Language Detection in Low-Resource Settings

Summary

Most of the world's languages have no tools for automatic content moderation. This paper builds such a resource for Tigrinya, which has about 10 million speakers. The authors release TiALD, a set of 13,717 YouTube comments. Native speakers labeled every comment for three things at once: whether it is abusive, its sentiment, and its topic. Many Tigrinya users write in improvised Latin transliteration instead of Ge'ez script, so the corpus keeps both writing systems. Abuse is rare in random samples, so the authors chose comments for annotation by growing a small set of seed terms through word2vec neighbourhoods. This gives a pool that is more balanced and more varied in vocabulary. The test set is triple-checked and its disagreements are adjudicated by experts. In the baselines, compact fine-tuned models clearly beat prompted frontier LLMs, reaching 86.67% F1 on abusiveness. Training the three tasks jointly helps, and so do machine-written video descriptions. The benchmark gives low-resource safety research a reproducible template and an open dataset.

Key points

  • The source pool was 4.1M comments from 51 channels. The final TiALD set has 13,717 comments drawn from 7,373 videos with over 1.2B views, labeled by nine native annotators (4 female, 5 male).
  • Script mix: about 64% of the source comments contain Romanized text. The annotated data is roughly 70% Ge'ez and 30% Latin or mixed script. Non-Tigrinya comments were removed with GeezSwitch.
  • Sampling started from 61 seed terms, expanded in three rounds with a word2vec CBOW model (300 dimensions, Gensim). Each round took fewer neighbours: 50, then 25, then 10. The result was 8,728 terms, used to pick 15K comments, plus 5K random comments as a control. The type-to-token ratio was 0.28, against 0.13 for random sampling.
  • Inter-annotator agreement (Cohen's κ): 0.758 for abusiveness, 0.649 for sentiment, 0.603 for topic. Topic disagreements mostly came from comments that fit several topics.
  • The 900-comment test set got gold labels through adjudication: the first two annotators fully agreed on 546 comments. The best abusiveness F1 is 86.67%, more than 7 points above the best LLM. Joint multi-task training and video descriptions (Qwen-2.5-VL 3B, refined with GPT-4o) both improve results.

Interesting ideas

  • I1. Grow a candidate vocabulary from a few seeds by walking embedding neighbours in shrinking steps, dropping mere inflections at each step. Keep a random control slice and check diversity with type-to-token ratio. This finds rare phenomena without a curated lexicon or a narrow keyword list. — For Mova (learn): Use this to pick sentences for silver annotation that contain rare constructions or verb classes. Keep a random control slice so that measured gains are not just artefacts of the selection.
  • I2. Single-annotate the training data, but double-annotate the test data and send disagreements to separate experts. This spends the expensive effort where evaluation reliability depends on it. — For Mova (learn): Sentences where the witness parsers disagree could go to the top LLM teacher tier or to a human. Only those adjudicated items would enter the held-out evaluation set.
  • I3. Label several related dimensions of the same item together. The tasks then share signal, and the error analysis can explain one label by another. — For Mova (frames): The prag crate could predict speech act, sentiment and irony jointly for an utterance. It could then check that they agree, for example a positive surface sentiment combined with an irony flag.
  • I4. Look at why annotators disagree, not only at the agreement scores. Here most disagreements came from items that legitimately belong to several categories, which points to a flaw in the label scheme rather than annotator noise. — For Mova (world): When the ontology mapping or frame assignment gets conflicting labels, check first whether the entity or event truly belongs to several classes. If so, allow multiple types instead of forcing one.
  • I5. Build the data around the spelling people actually use, nonstandard transliteration included, rather than normalizing it away. Models trained on clean text alone break on real input. — For Mova (tokens): Add informal spellings, mixed casing and transliterated names to the tokenizer and tagger test sets. That will show where the crate en fails on noisy real-world text.

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

Fields: Linguistics & languages

Cited by

3 papers of our library cite it (of the 140884 whose bibliographies we read); 1 of them have a page here, 2 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2505.12116 · arXiv 2505.12116

@misc{gaim2025multi,
  title = {{A Multi-Task Benchmark for Abusive Language Detection in Low-Resource Settings}},
  author = {Gaim, Fitsum and Song, Hoyun and Lee, Huije and Ko, Changgeon and Hwang, Eui Jun and Park, Jong C.},
  year = {2025},
  doi = {10.48550/arxiv.2505.12116},
  eprint = {2505.12116},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2505.12116}
}

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