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- W4385570023 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." @default.
- W4385570023 created "2023-08-05" @default.
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- W4385570023 date "2023-01-01" @default.
- W4385570023 modified "2023-09-26" @default.
- W4385570023 title "MarsEclipse at SemEval-2023 Task 3: Multi-lingual and Multi-label Framing Detection with Contrastive Learning" @default.
- W4385570023 doi "https://doi.org/10.18653/v1/2023.semeval-1.10" @default.
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