Team TheSyllogist at SemEval-2023 Task 3: Language-Agnostic Framing Detection in Multi-Lingual Online News: A Zero-Shot Transfer Approach

Authors

Osama Mohammed Afzal, Mohamed Bin Zayed University of Artificial IntelligenceFollow
Preslav Nakov, Mohamed Bin Zayed University of Artificial IntelligenceFollow

Document Type

Conference Proceeding

Publication Title

17th International Workshop on Semantic Evaluation, SemEval 2023 - Proceedings of the Workshop

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.

First Page

2058

Last Page

2061

Publication Date

7-2023

Keywords

Translation (languages), Zero-shot learning, Semantics

Comments

Archived thanks to ACL Anthology

License: CC by 4.0

Uploaded: April 03, 2024

Recommended Citation

O. Afzal and P. Nakov, "Team TheSyllogist at SemEval-2023 Task 3: Language-Agnostic Framing Detection in Multi-Lingual Online News: A Zero-Shot Transfer Approach," 17th International Workshop on Semantic Evaluation, SemEval 2023 - Proceedings of the Workshop, pp. 2058 - 2061, Jul 2023.

Additional Links

DOI link: https://doi.org/10.18653/v1/2023.semeval-1.283