@inproceedings{liao-etal-2023-marseclipse,
title = "{M}ars{E}clipse at {S}em{E}val-2023 Task 3: Multi-lingual and Multi-label Framing Detection with Contrastive Learning",
author = "Liao, Qisheng and
Lai, Meiting and
Nakov, Preslav",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Da San Martino, Giovanni and
Tayyar Madabushi, Harish and
Kumar, Ritesh and
Sartori, Elisa},
booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.semeval-1.10",
doi = "10.18653/v1/2023.semeval-1.10",
pages = "83--87",
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 \url{https://github.com/QishengL/SemEval2023}.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T MarsEclipse at SemEval-2023 Task 3: Multi-lingual and Multi-label Framing Detection with Contrastive Learning
%A Liao, Qisheng
%A Lai, Meiting
%A Nakov, Preslav
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Da San Martino, Giovanni
%Y Tayyar Madabushi, Harish
%Y Kumar, Ritesh
%Y Sartori, Elisa
%S Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F liao-etal-2023-marseclipse
%X 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.
%R 10.18653/v1/2023.semeval-1.10
%U https://aclanthology.org/2023.semeval-1.10
%U https://doi.org/10.18653/v1/2023.semeval-1.10
%P 83-87
Markdown (Informal)
[MarsEclipse at SemEval-2023 Task 3: Multi-lingual and Multi-label Framing Detection with Contrastive Learning](https://aclanthology.org/2023.semeval-1.10) (Liao et al., SemEval 2023)
ACL