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Human-in-the-Loop for Data Collection: a Multi-Target Counter Narrative Dataset to Fight Online Hate Speech

Paper · 2021 · Margherita Fanton, Helena Bonaldi, Serra Sinem Tekiroğlu et al.

Summary

Counter narratives (CNs) are calm, fact-based replies to online hate. Automating their generation requires training data, but earlier HS/CN collections offered either expert quality or large size, not both. The authors extend the author-reviewer framework of Tekiroğlu et al. (2020) into a human-in-the-loop (HITL) pipeline. A GPT-2 "author" proposes hate speech / counter narrative pairs. Trained reviewers then accept, post-edit or reject each pair, and the validated output is fed back to retrain the model from scratch for the next round.

Over two sessions covering about six months, the team first ran repeated sequential loops. They then tried alternative author configurations in parallel to counter weaknesses observed in the first session. Reviewing became more efficient with each iteration, with fewer discarded pairs and lighter post-editing, but novelty and diversity gradually declined. The adapted configurations restored much of that quality without a major efficiency cost. The outcome is a 5000-pair English resource spanning many hate targets, presented as the first expert-based multi-target HS/CN dataset.

Key points

  • Seed data (V1): 880 HS/CN pairs written by 20 operators from two NGOs. It covers targets such as JEWS, MUSLIM, LGBT+, WOMEN, MIGRANTS, ROMANI, PEOPLE OF COLOR, DISABLED and OVERWEIGHT.
  • Author model: GPT-2 medium, fine-tuned for 3 epochs at a learning rate of 2e-5, with Nucleus Sampling (p = 0.9) used for generation.
  • Session one: four iterative loops (V2–V5) produced 3000 pairs. Session two: four parallel V6 configurations, each starting from V5, added 500 pairs apiece for a total of 5000.
  • Reviewers: three intern annotators, given two weeks of NGO-guided training and working over 18 weeks. Efficiency was tracked with acceptance rate and HTER, and the generator kept adding new vocabulary, including words carried over from other targets.
  • Availability: the dataset is released at github.com/marcoguerini/CONAN.

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 TrentoTrento · Italy62 works · 14,868 citations🔬Fondazione Bruno KesslerTrento · Italy25 works · 43,498 citations

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

🧭 Topics 2 Sentiment & emotion · 608 works Bias, fairness & safety · 209 works

Cite

Cite as Margherita Fanton, Helena Bonaldi, Serra Sinem Tekiroğlu and Marco Guerini. 2021. Human-in-the-Loop for Data Collection: a Multi-Target Counter Narrative Dataset to Fight Online Hate Speech. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2021.acl-long.250

@inproceedings{fanton2021human,
  title = {{Human-in-the-Loop for Data Collection: a Multi-Target Counter Narrative Dataset to Fight Online Hate Speech}},
  author = {Margherita Fanton, Helena Bonaldi, Serra Sinem Tekiroğlu and Marco Guerini},
  booktitle = {Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  year = {2021},
  doi = {10.18653/v1/2021.acl-long.250},
  url = {https://doi.org/10.18653/v1/2021.acl-long.250}
}

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