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Overview of the HASOC Subtrack at FIRE 2021: Hate Speech and Offensive Content Identification in English and Indo-Aryan Languages

Paper · 2021 · Thomas Mandl, Sandip Modha, Gautam Kishore Shahi et al.

Summary

HASOC 2021 is a shared task at FIRE that benchmarks the automatic detection of hateful and offensive posts. It re-runs the English and Hindi tasks from 2019 and 2020 and adds Marathi as a new language. The organisers collected tweets during India's severe second COVID-19 wave and the West Bengal post-election violence, using trending hashtags to steer the collection. To find candidate posts, they used a deliberately weak SVM n-gram filter, then topped up the sample with random and profanity-keyword tweets to balance the classes. At least two annotators labelled each tweet. There are two tasks:

  • Task A asks whether a post is offensive or not, in all three languages.
  • Task B splits offensive posts into hate, offence, and profanity, for English and Hindi only.

In total, 65 teams submitted 652 runs. Transformer models won everywhere, and cross-lingual transfer from Hindi gave the best Marathi result. The task matters because it adds fresh, topical labelled data for under-served Indian languages. It also highlights how poorly detectors carry over from one dataset to another.

Key points

  • Best Task A macro F1 was 0.83 for English (NLP-CIC, 0.8305), 0.78 for Hindi, and 0.91 for Marathi (WLV-RIT, 0.9144). Best Task B English score was only 0.6657.
  • Task A labels are HOF vs NOT. Task B labels are:
    • HATE: negative traits attributed to a group.
    • OFFN: insulting or degrading an individual.
    • PRFN: swearing without an insult.
  • Sampling used an SVM n-gram classifier trained on HASOC 2019/2020 data, tuned to an F1 of roughly 0.5. The final sample combined tweets it flagged as HOF, 5% of tweets it did not flag, and extra profanity-keyword tweets.
  • The best Marathi system fine-tuned XLM-R Large with transfer learning. Transfer from Hindi (HASOC 2019) worked better than transfer from English (OffensEval 2019). The runner-up found that LaBSE (0.8808) beat XLM-R on Marathi alone, but XLM-R was stronger when Hindi and Marathi data were pooled.
  • Score spread among the top 10 systems was far smaller than among all systems. For Marathi, the standard deviation was 0.0143 for the top 10 versus 0.0774 overall. The related work cites cross-dataset performance drops of over 30% (Fortuna et al.).

Interesting ideas

  • I1. The organisers used a cheap, intentionally mediocre classifier to concentrate rare positives in a huge stream, and mixed in a small random slice of rejected items to avoid a closed loop.
  • I2. Transfer from a closely related language (Hindi → Marathi) beat transfer from a high-resource but distant one (English).
  • I3. The fine-grained labels are defined by the target of the abuse: a group (HATE), an individual (OFFN), or nobody (PRFN). This is a structural rather than lexical criterion.
  • I4. Disagreement between annotators is treated as a natural sign of dubious items rather than proof of error. The paper also notes that agreement scores alone do not guarantee good labels.
  • I5. Comparing the spread of top-10 systems with the spread of all systems shows whether the leading differences are meaningful or just noise.

For Mova

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

  • I1 (learn): When mining silver sentences for rare constructions such as ditransitives or garden paths, a crude filter plus a fixed random share of rejected items would give both enriched data and an unbiased control.
  • I2 (tree): When converting or borrowing treebanks for gparse, prefer sources closest in register and dialect over the largest ones, and measure that choice explicitly.
  • I3 (frames): prag could classify insulting speech acts by reading the patient/target role from the frame, which keeps the decision explainable instead of relying on word lists.
  • I4 (learn): Sentences where witness parsers disagree should be routed to a "doubtful" bucket and kept out of training, rather than forced into a majority vote.
  • I5 (learn): When comparing parser or reader variants, report the variance across seeds or runs next to the deltas, and check robustness on a second, out-of-domain test set.

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

🎓University of WolverhamptonWolverhampton · United Kingdom30 works · 6,272 citations🎓University of LeedsLeeds · United Kingdom21 works · 1,343 citations🎓Indian Institute of Science BangaloreBengaluru · India8 works · 1,140 citations🎓Rochester Institute of TechnologyRochester · United States10 works · 4,906 citations🎓University of HildesheimHildesheim · Germany3 works · 64 citations

Also on the byline: University of Duisburg-Essen · University of Bamberg · Dhirubhai Ambani University · University of Bedfordshire · LDRP Institute of Technology and Research.

📚 Shelves 4 Computer Science · 9,086 works Artificial Intelligence · 8,889 works Social Sciences · 744 works Sociology and Political Science · 221 works

🧭 Topics 4 English · 1,373 works Sentiment & emotion · 608 works Evaluation & benchmarks · 685 works Bias, fairness & safety · 209 works

Cite

Cite as Thomas Mandl, Sandip Modha, Gautam Kishore Shahi, Hiren Madhu, Shrey Satapara, Prasenjit Majumder, Johannes Schaefer, Tharindu Ranasinghe, Marcos Zampieri, Durgesh Nandini and Amit Kumar Jaiswal. 2021. Overview of the HASOC Subtrack at FIRE 2021: Hate Speech and Offensive Content Identification in English and Indo-Aryan Languages. arXiv:2112.09301.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2112.09301 · arXiv 2112.09301

@misc{mandl2021overview,
  title = {{Overview of the HASOC Subtrack at FIRE 2021: Hate Speech and Offensive Content Identification in English and Indo-Aryan Languages}},
  author = {Thomas Mandl, Sandip Modha, Gautam Kishore Shahi, Hiren Madhu, Shrey Satapara, Prasenjit Majumder, Johannes Schaefer, Tharindu Ranasinghe, Marcos Zampieri, Durgesh Nandini and Amit Kumar Jaiswal},
  year = {2021},
  doi = {10.48550/arxiv.2112.09301},
  eprint = {2112.09301},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2112.09301}
}

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