@inproceedings{kim-bethard-2020-ttui,
title = "{TTUI} at {S}em{E}val-2020 Task 11: Propaganda Detection with Transfer Learning and Ensembles",
author = "Kim, Moonsung and
Bethard, Steven",
editor = "Herbelot, Aurelie and
Zhu, Xiaodan and
Palmer, Alexis and
Schneider, Nathan and
May, Jonathan and
Shutova, Ekaterina",
booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
month = dec,
year = "2020",
address = "Barcelona (online)",
publisher = "International Committee for Computational Linguistics",
url = "https://aclanthology.org/2020.semeval-1.240",
doi = "10.18653/v1/2020.semeval-1.240",
pages = "1829--1834",
abstract = "In this paper, we describe our approaches and systems for the SemEval-2020 Task 11 on propaganda technique detection. We fine-tuned BERT and RoBERTa pre-trained models then merged them with an average ensemble. We conducted several experiments for input representations dealing with long texts and preserving context as well as for the imbalanced class problem. Our system ranked 20th out of 36 teams with 0.398 F1 in the SI task and 14th out of 31 teams with 0.556 F1 in the TC task.",
}
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<abstract>In this paper, we describe our approaches and systems for the SemEval-2020 Task 11 on propaganda technique detection. We fine-tuned BERT and RoBERTa pre-trained models then merged them with an average ensemble. We conducted several experiments for input representations dealing with long texts and preserving context as well as for the imbalanced class problem. Our system ranked 20th out of 36 teams with 0.398 F1 in the SI task and 14th out of 31 teams with 0.556 F1 in the TC task.</abstract>
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%0 Conference Proceedings
%T TTUI at SemEval-2020 Task 11: Propaganda Detection with Transfer Learning and Ensembles
%A Kim, Moonsung
%A Bethard, Steven
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y May, Jonathan
%Y Shutova, Ekaterina
%S Proceedings of the Fourteenth Workshop on Semantic Evaluation
%D 2020
%8 December
%I International Committee for Computational Linguistics
%C Barcelona (online)
%F kim-bethard-2020-ttui
%X In this paper, we describe our approaches and systems for the SemEval-2020 Task 11 on propaganda technique detection. We fine-tuned BERT and RoBERTa pre-trained models then merged them with an average ensemble. We conducted several experiments for input representations dealing with long texts and preserving context as well as for the imbalanced class problem. Our system ranked 20th out of 36 teams with 0.398 F1 in the SI task and 14th out of 31 teams with 0.556 F1 in the TC task.
%R 10.18653/v1/2020.semeval-1.240
%U https://aclanthology.org/2020.semeval-1.240
%U https://doi.org/10.18653/v1/2020.semeval-1.240
%P 1829-1834
Markdown (Informal)
[TTUI at SemEval-2020 Task 11: Propaganda Detection with Transfer Learning and Ensembles](https://aclanthology.org/2020.semeval-1.240) (Kim & Bethard, SemEval 2020)
ACL