@inproceedings{morio-etal-2020-hitachi-semeval,
title = "Hitachi at {S}em{E}val-2020 Task 11: An Empirical Study of Pre-Trained Transformer Family for Propaganda Detection",
author = "Morio, Gaku and
Morishita, Terufumi and
Ozaki, Hiroaki and
Miyoshi, Toshinori",
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-1.228",
doi = "10.18653/v1/2020.semeval-1.228",
pages = "1739--1748",
abstract = "In this paper, we show our system for SemEval-2020 task 11, where we tackle propaganda span identification (SI) and technique classification (TC). We investigate heterogeneous pre-trained language models (PLMs) such as BERT, GPT-2, XLNet, XLM, RoBERTa, and XLM-RoBERTa for SI and TC fine-tuning, respectively. In large-scale experiments, we found that each of the language models has a characteristic property, and using an ensemble model with them is promising. Finally, the ensemble model was ranked 1st amongst 35 teams for SI and 3rd amongst 31 teams for TC.",
}
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<abstract>In this paper, we show our system for SemEval-2020 task 11, where we tackle propaganda span identification (SI) and technique classification (TC). We investigate heterogeneous pre-trained language models (PLMs) such as BERT, GPT-2, XLNet, XLM, RoBERTa, and XLM-RoBERTa for SI and TC fine-tuning, respectively. In large-scale experiments, we found that each of the language models has a characteristic property, and using an ensemble model with them is promising. Finally, the ensemble model was ranked 1st amongst 35 teams for SI and 3rd amongst 31 teams for TC.</abstract>
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%0 Conference Proceedings
%T Hitachi at SemEval-2020 Task 11: An Empirical Study of Pre-Trained Transformer Family for Propaganda Detection
%A Morio, Gaku
%A Morishita, Terufumi
%A Ozaki, Hiroaki
%A Miyoshi, Toshinori
%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 morio-etal-2020-hitachi-semeval
%X In this paper, we show our system for SemEval-2020 task 11, where we tackle propaganda span identification (SI) and technique classification (TC). We investigate heterogeneous pre-trained language models (PLMs) such as BERT, GPT-2, XLNet, XLM, RoBERTa, and XLM-RoBERTa for SI and TC fine-tuning, respectively. In large-scale experiments, we found that each of the language models has a characteristic property, and using an ensemble model with them is promising. Finally, the ensemble model was ranked 1st amongst 35 teams for SI and 3rd amongst 31 teams for TC.
%R 10.18653/v1/2020.semeval-1.228
%U https://aclanthology.org/2020.semeval-1.228
%U https://doi.org/10.18653/v1/2020.semeval-1.228
%P 1739-1748
Markdown (Informal)
[Hitachi at SemEval-2020 Task 11: An Empirical Study of Pre-Trained Transformer Family for Propaganda Detection](https://aclanthology.org/2020.semeval-1.228) (Morio et al., SemEval 2020)
ACL