@inproceedings{ivacic-etal-2024-comparing,
title = "Comparing News Framing of Migration Crises using Zero-Shot Classification",
author = "Iva{\v{c}}i{\v{c}}, Nikola and
Purver, Matthew and
Lind, Fabienne and
Pollak, Senja and
Boomgaarden, Hajo and
Bajt, Veronika",
editor = "Sommerauer, Pia and
Caselli, Tommaso and
Nissim, Malvina and
Remijnse, Levi and
Vossen, Piek",
booktitle = "Proceedings of the First Workshop on Reference, Framing, and Perspective @ LREC-COLING 2024",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.rfp-1.3",
pages = "18--27",
abstract = "We present an experiment on classifying news frames in a language unseen by the learner, using zero-shot cross-lingual transfer learning. We used two pre-trained multilingual Transformer Encoder neural network models and tested with four specific news frames, investigating two approaches to the resulting multi-label task: Binary Relevance (treating each frame independently) and Label Power-set (predicting each possible combination of frames). We train our classifiers on an available annotated multilingual migration news dataset and test on an unseen Slovene language migration news corpus, first evaluating performance and then using the classifiers to analyse how media framed the news during the periods of Syria and Ukraine conflict-related migrations.",
}
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<abstract>We present an experiment on classifying news frames in a language unseen by the learner, using zero-shot cross-lingual transfer learning. We used two pre-trained multilingual Transformer Encoder neural network models and tested with four specific news frames, investigating two approaches to the resulting multi-label task: Binary Relevance (treating each frame independently) and Label Power-set (predicting each possible combination of frames). We train our classifiers on an available annotated multilingual migration news dataset and test on an unseen Slovene language migration news corpus, first evaluating performance and then using the classifiers to analyse how media framed the news during the periods of Syria and Ukraine conflict-related migrations.</abstract>
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%0 Conference Proceedings
%T Comparing News Framing of Migration Crises using Zero-Shot Classification
%A Ivačič, Nikola
%A Purver, Matthew
%A Lind, Fabienne
%A Pollak, Senja
%A Boomgaarden, Hajo
%A Bajt, Veronika
%Y Sommerauer, Pia
%Y Caselli, Tommaso
%Y Nissim, Malvina
%Y Remijnse, Levi
%Y Vossen, Piek
%S Proceedings of the First Workshop on Reference, Framing, and Perspective @ LREC-COLING 2024
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F ivacic-etal-2024-comparing
%X We present an experiment on classifying news frames in a language unseen by the learner, using zero-shot cross-lingual transfer learning. We used two pre-trained multilingual Transformer Encoder neural network models and tested with four specific news frames, investigating two approaches to the resulting multi-label task: Binary Relevance (treating each frame independently) and Label Power-set (predicting each possible combination of frames). We train our classifiers on an available annotated multilingual migration news dataset and test on an unseen Slovene language migration news corpus, first evaluating performance and then using the classifiers to analyse how media framed the news during the periods of Syria and Ukraine conflict-related migrations.
%U https://aclanthology.org/2024.rfp-1.3
%P 18-27
Markdown (Informal)
[Comparing News Framing of Migration Crises using Zero-Shot Classification](https://aclanthology.org/2024.rfp-1.3) (Ivačič et al., rfp-WS 2024)
ACL
- Nikola Ivačič, Matthew Purver, Fabienne Lind, Senja Pollak, Hajo Boomgaarden, and Veronika Bajt. 2024. Comparing News Framing of Migration Crises using Zero-Shot Classification. In Proceedings of the First Workshop on Reference, Framing, and Perspective @ LREC-COLING 2024, pages 18–27, Torino, Italia. ELRA and ICCL.