@inproceedings{kaczynski-przybyla-2021-homados,
title = "{HOMADOS} at {S}em{E}val-2021 Task 6: Multi-Task Learning for Propaganda Detection",
author = "Kaczy{\'n}ski, Konrad and
Przyby{\l}a, Piotr",
editor = "Palmer, Alexis and
Schneider, Nathan and
Schluter, Natalie and
Emerson, Guy and
Herbelot, Aurelie and
Zhu, Xiaodan",
booktitle = "Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.semeval-1.141",
doi = "10.18653/v1/2021.semeval-1.141",
pages = "1027--1031",
abstract = "Among the tasks motivated by the proliferation of misinformation, propaganda detection is particularly challenging due to the deficit of fine-grained manual annotations required to train machine learning models. Here we show how data from other related tasks, including credibility assessment, can be leveraged in multi-task learning (MTL) framework to accelerate the training process. To that end, we design a BERT-based model with multiple output layers, train it in several MTL scenarios and perform evaluation against the SemEval gold standard.",
}
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<abstract>Among the tasks motivated by the proliferation of misinformation, propaganda detection is particularly challenging due to the deficit of fine-grained manual annotations required to train machine learning models. Here we show how data from other related tasks, including credibility assessment, can be leveraged in multi-task learning (MTL) framework to accelerate the training process. To that end, we design a BERT-based model with multiple output layers, train it in several MTL scenarios and perform evaluation against the SemEval gold standard.</abstract>
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%0 Conference Proceedings
%T HOMADOS at SemEval-2021 Task 6: Multi-Task Learning for Propaganda Detection
%A Kaczyński, Konrad
%A Przybyła, Piotr
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y Schluter, Natalie
%Y Emerson, Guy
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%S Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F kaczynski-przybyla-2021-homados
%X Among the tasks motivated by the proliferation of misinformation, propaganda detection is particularly challenging due to the deficit of fine-grained manual annotations required to train machine learning models. Here we show how data from other related tasks, including credibility assessment, can be leveraged in multi-task learning (MTL) framework to accelerate the training process. To that end, we design a BERT-based model with multiple output layers, train it in several MTL scenarios and perform evaluation against the SemEval gold standard.
%R 10.18653/v1/2021.semeval-1.141
%U https://aclanthology.org/2021.semeval-1.141
%U https://doi.org/10.18653/v1/2021.semeval-1.141
%P 1027-1031
Markdown (Informal)
[HOMADOS at SemEval-2021 Task 6: Multi-Task Learning for Propaganda Detection](https://aclanthology.org/2021.semeval-1.141) (Kaczyński & Przybyła, SemEval 2021)
ACL