mlsanityData to 7 Oct 2026

Is More Data Better? Re-thinking the Importance of Efficiency in Abusive Language Detection with Transformers-Based Active Learning

arXiv 2209.10193 ↗First posted 21 Sep 2022cs.CLcited by 4

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Abstract (arXiv)

Annotating abusive language is expensive, logistically complex and creates a risk of psychological harm. However, most machine learning research has prioritized maximizing effectiveness (i.e., F1 or accuracy score) rather than data efficiency (i.e., minimizing the amount of data that is annotated). In this paper, we use simulated experiments over two datasets at varying percentages of abuse to demonstrate that transformers-based active learning is a promising approach to substantially raise efficiency whilst still maintaining high effectiveness, especially when abusive content is a smaller percentage of the dataset. This approach requires a fraction of labeled data to reach performance equivalent to training over the full dataset.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 17 of its 42 references that are in the map. First posted on arXiv 21 Sep 2022.

Builds on

17 of 42 references.

References · 17

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,243
  4. 2020Making Pre-trained Language Models Better Few-shot Learnerscited by 1,255
  5. 2017Automated Hate Speech Detection and the Problem of Offensive Languagecited by 724
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  12. 2019A BERT-Based Transfer Learning Approach for Hate Speech Detection in Online Social Mediacited by 95
5 more
  1. 2018Hate Lingo: A Target-based Linguistic Analysis of Hate Speech in Social Mediacited by 59
  2. 2021Revisiting Uncertainty-based Query Strategies for Active Learning with Transformerscited by 59
  3. 2018Active Learning with Partial Feedbackcited by 27
  4. 2021Small-Text: Active Learning for Text Classification in Pythoncited by 18
  5. 2021An Information Retrieval Approach to Building Datasets for Hate Speech Detectioncited by 9

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Cited by year

Citing papers per year: 2023: 4.

4 papers.

  1. 2023SemEval-2023 Task 10: Explainable Detection of Online Sexismcited by 48
  2. 2023Investigating Multi-source Active Learning for Natural Language Inferencecited by 6
  3. 2023Evaluating the Effectiveness of Natural Language Inference for Hate Speech Detection in Languages with Limited Labeled Datacited by 1
  4. 2023Mining of Single-Class by Active Learning for Semantic Segmentationcited by 0