NAP at SemEval-2023 Task 3: Is Less Really More? (Back-)Translation as Data Augmentation Strategies for Detecting Persuasion Techniques
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Abstract (arXiv)
Persuasion techniques detection in news in a multi-lingual setup is non-trivial and comes with challenges, including little training data. Our system successfully leverages (back-)translation as data augmentation strategies with multi-lingual transformer models for the task of detecting persuasion techniques. The automatic and human evaluation of our augmented data allows us to explore whether (back-)translation aid or hinder performance. Our in-depth analyses indicate that both data augmentation strategies boost performance; however, balancing human-produced and machine-generated data seems to be crucial.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 9 of its 29 references that are in the map. First posted on arXiv 27 Apr 2023.
Builds on
9 of 29 references.
References · 9
- 2019Parameter-Efficient Transfer Learning for NLPcited by 4,236
- 2020AdapterHub: A Framework for Adapting Transformerscited by 444
- 2018Marian: Fast Neural Machine Translation in C++cited by 203
- 2022Efficient Few-Shot Learning Without Promptscited by 137
- 2020SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articlescited by 81
- 2021SemEval-2021 Task 6: Detection of Persuasion Techniques in Texts and Imagescited by 50
- 2019Findings of the NLP4IF-2019 Shared Task on Fine-Grained Propaganda Detectioncited by 24
- 2020ApplicaAI at SemEval-2020 Task 11: On RoBERTa-CRF, Span CLS and Whether Self-Training Helps Themcited by 16
- 2020aschern at SemEval-2020 Task 11: It Takes Three to Tango: RoBERTa, CRF, and Transfer Learningcited by 7
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Citing papers per year: 2023: 1.
1 paper.