mlsanityData to 14 Oct 2026

Xu at SemEval-2022 Task 4: Pre-BERT Neural Network Methods vs Post-BERT RoBERTa Approach for Patronizing and Condescending Language Detection

arXiv 2211.06874 ↗First posted 13 Nov 2022cs.CLcited by 3

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Abstract (arXiv)

This paper describes my participation in the SemEval-2022 Task 4: Patronizing and Condescending Language Detection. I participate in both subtasks: Patronizing and Condescending Language (PCL) Identification and Patronizing and Condescending Language Categorization, with the main focus put on subtask 1. The experiments compare pre-BERT neural network (NN) based systems against post-BERT pretrained language model RoBERTa. This research finds NN-based systems in the experiments perform worse on the task compared to the pretrained language models. The top-performing RoBERTa system is ranked 26 out of 78 teams (F1-score: 54.64) in subtask 1, and 23 out of 49 teams (F1-score: 30.03) in subtask 2.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 5 of its 21 references that are in the map. First posted on arXiv 13 Nov 2022.

Builds on

5 of 21 references.

References · 5

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  3. 2019SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)cited by 207
  4. 2019TalkDown: A Corpus for Condescension Detection in Contextcited by 21
  5. 2020Don't Patronize Me! An Annotated Dataset with Patronizing and Condescending Language towards Vulnerable Communitiescited by 19

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Citing papers per year: 2023: 1; 2024: 2.

3 papers.