Abstract
This paper describes a multilingual persuasion detection system that incorporates persuasion technique attributes for a multi-label classification task. The proposed method has two advantages. First, it combines persuasion features with a sequence classification transformer to classify persuasion techniques. Second, it is a language agnostic approach that supports a total of 100 languages, guaranteed by the multilingual transformer module and the Google translator interface. We found that our persuasion system outperformed the SemEval baseline in all languages except zero shot prediction languages, which did not constitute the main focus of our research. With the highest F1-Micro score of 0.45, Italian achieved the eighth position on the leaderboard.- Anthology ID:
- 2023.semeval-1.293
- 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:
- 2124–2132
- Language:
- URL:
- https://aclanthology.org/2023.semeval-1.293
- DOI:
- 10.18653/v1/2023.semeval-1.293
- Cite (ACL):
- Fatima Zahra Qachfar and Rakesh Verma. 2023. ReDASPersuasion at SemEval-2023 Task 3: Persuasion Detection using Multilingual Transformers and Language Agnostic Features. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 2124–2132, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- ReDASPersuasion at SemEval-2023 Task 3: Persuasion Detection using Multilingual Transformers and Language Agnostic Features (Qachfar & Verma, SemEval 2023)
- PDF:
- https://preview.aclanthology.org/landing_page/2023.semeval-1.293.pdf