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Taygete at SemEval-2022 Task 4: RoBERTa based models for detecting Patronising and Condescending Language

arXiv 2204.10519 ↗First posted 22 Apr 2022cs.CL cs.LGcited by 0

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Abstract (arXiv)

This work describes the development of different models to detect patronising and condescending language within extracts of news articles as part of the SemEval 2022 competition (Task-4). This work explores different models based on the pre-trained RoBERTa language model coupled with LSTM and CNN layers. The best models achieved 15th rank with an F1-score of 0.5924 for subtask-A and 12th in subtask-B with a macro-F1 score of 0.3763.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 12 of its 20 references that are in the map. First posted on arXiv 22 Apr 2022.

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12 of 20 references.

References · 12

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  2. 2019Class-Balanced Loss Based on Effective Number of Samplescited by 1,243
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  4. 2017Fake News Detection on Social Media: A Data Mining Perspectivecited by 335
  5. 2019Simple BERT Models for Relation Extraction and Semantic Role Labelingcited by 154
  6. 2020KUISAIL at SemEval-2020 Task 12: BERT-CNN for Offensive Speech Identification in Social Mediacited by 84
  7. 2015Effective LSTMs for Target-Dependent Sentiment Classificationcited by 82
  8. 2020A Survey on Text Classification: From Shallow to Deep Learningcited by 80
  9. 2020A Framework for the Computational Linguistic Analysis of Dehumanizationcited by 31
  10. 2019TalkDown: A Corpus for Condescension Detection in Contextcited by 21
  11. 2020Don't Patronize Me! An Annotated Dataset with Patronizing and Condescending Language towards Vulnerable Communitiescited by 19
  12. 2020ApplicaAI at SemEval-2020 Task 11: On RoBERTa-CRF, Span CLS and Whether Self-Training Helps Themcited by 16

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