Abstract
We describe our system for SemEval-2022 Task 3 subtask 2 which on detecting the frames used in a news article in a multi-lingual setup. We propose a multi-lingual approach based on machine translation of the input, followed by an English prediction model. Our system demonstrated good zero-shot transfer capability, achieving micro-F1 scores of 53% for Greek (4th on the leaderboard) and 56.1% for Georgian (3rd on the leaderboard), without any prior training on translated data for these languages. Moreover, our system achieved comparable performance on seven other languages, including German, English, French, Russian, Italian, Polish, and Spanish. Our results demonstrate the feasibility of creating a language-agnostic model for automatic framing detection in online news.- Anthology ID:
- 2023.semeval-1.283
- 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:
- 2058–2061
- Language:
- URL:
- https://aclanthology.org/2023.semeval-1.283
- DOI:
- 10.18653/v1/2023.semeval-1.283
- Cite (ACL):
- Osama Mohammed Afzal and Preslav Nakov. 2023. Team TheSyllogist at SemEval-2023 Task 3: Language-Agnostic Framing Detection in Multi-Lingual Online News: A Zero-Shot Transfer Approach. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 2058–2061, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Team TheSyllogist at SemEval-2023 Task 3: Language-Agnostic Framing Detection in Multi-Lingual Online News: A Zero-Shot Transfer Approach (Mohammed Afzal & Nakov, SemEval 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2023.semeval-1.283.pdf