MarsEclipse at SemEval-2023 Task 3: Multi-Lingual and Multi-Label Framing Detection with Contrastive Learning

Authors

Qisheng Liao, Mohamed bin Zayed University of Artificial IntelligenceFollow
Meiting Lai, Liaoning University
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

This paper describes our system for SemEval-2023 Task 3 Subtask 2 on Framing Detection. We used a multi-label contrastive loss for fine-tuning large pre-trained language models in a multi-lingual setting, achieving very competitive results: our system was ranked first on the official test set and on the official shared task leaderboard for five of the six languages for which we had training data and for which we could perform fine-tuning. Here, we describe our experimental setup, as well as various ablation studies. The code of our system is available at https://github.com/QishengL/ SemEval2023.

First Page

83

Last Page

87

Publication Date

1-1-2023

Recommended Citation

Q. Liao et al., "MarsEclipse at SemEval-2023 Task 3: Multi-Lingual and Multi-Label Framing Detection with Contrastive Learning," 17th International Workshop on Semantic Evaluation, SemEval 2023 - Proceedings of the Workshop, pp. 83 - 87, Jan 2023.