Abstract
We describe our submission to SemEval 2023 Task 3, specifically the subtask on persuasion technique detection. In this work, our team NLUBot101 tackled a novel task of classifying persuasion techniques in online news articles at a paragraph level. The low-resource multilingual datasets, along with the imbalanced label distribution, make this task challenging. Our team presented a cross-lingual data augmentation approach and leveraged a recently proposed multilingual natural language inference model to address these challenges. Our solution achieves the highest macro-F1 score for the English task, and top 5 micro-F1 scores on both the English and Russian leaderboards.- Anthology ID:
- 2023.semeval-1.227
- Volume:
- Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Atul Kr. Ojha, A. Seza Doğruöz, Giovanni Da San Martino, Harish Tayyar Madabushi, Ritesh Kumar, Elisa Sartori
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1636–1643
- Language:
- URL:
- https://aclanthology.org/2023.semeval-1.227
- DOI:
- 10.18653/v1/2023.semeval-1.227
- Cite (ACL):
- Genglin Liu, Yi Fung, and Heng Ji. 2023. NLUBot101 at SemEval-2023 Task 3: An Augmented Multilingual NLI Approach Towards Online News Persuasion Techniques Detection. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 1636–1643, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- NLUBot101 at SemEval-2023 Task 3: An Augmented Multilingual NLI Approach Towards Online News Persuasion Techniques Detection (Liu et al., SemEval 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2023.semeval-1.227.pdf