@inproceedings{koreeda-etal-2023-hitachi,
title = "Hitachi at {S}em{E}val-2023 Task 3: Exploring Cross-lingual Multi-task Strategies for Genre and Framing Detection in Online News",
author = "Koreeda, Yuta and
Yokote, Ken-ichi and
Ozaki, Hiroaki and
Yamaguchi, Atsuki and
Tsunokake, Masaya and
Sogawa, Yasuhiro",
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-1.237",
doi = "10.18653/v1/2023.semeval-1.237",
pages = "1702--1711",
abstract = "This paper explains the participation of team Hitachi to SemEval-2023 Task 3 {``}Detecting the genre, the framing, and the persuasion techniques in online news in a multi-lingual setup.{''} Based on the multilingual, multi-task nature of the task and the low-resource setting, we investigated different cross-lingual and multi-task strategies for training the pretrained language models. Through extensive experiments, we found that (a) cross-lingual/multi-task training, and (b) collecting an external balanced dataset, can benefit the genre and framing detection. We constructed ensemble models from the results and achieved the highest macro-averaged F1 scores in Italian and Russian genre categorization subtasks.",
}
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<abstract>This paper explains the participation of team Hitachi to SemEval-2023 Task 3 “Detecting the genre, the framing, and the persuasion techniques in online news in a multi-lingual setup.” Based on the multilingual, multi-task nature of the task and the low-resource setting, we investigated different cross-lingual and multi-task strategies for training the pretrained language models. Through extensive experiments, we found that (a) cross-lingual/multi-task training, and (b) collecting an external balanced dataset, can benefit the genre and framing detection. We constructed ensemble models from the results and achieved the highest macro-averaged F1 scores in Italian and Russian genre categorization subtasks.</abstract>
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%0 Conference Proceedings
%T Hitachi at SemEval-2023 Task 3: Exploring Cross-lingual Multi-task Strategies for Genre and Framing Detection in Online News
%A Koreeda, Yuta
%A Yokote, Ken-ichi
%A Ozaki, Hiroaki
%A Yamaguchi, Atsuki
%A Tsunokake, Masaya
%A Sogawa, Yasuhiro
%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 koreeda-etal-2023-hitachi
%X This paper explains the participation of team Hitachi to SemEval-2023 Task 3 “Detecting the genre, the framing, and the persuasion techniques in online news in a multi-lingual setup.” Based on the multilingual, multi-task nature of the task and the low-resource setting, we investigated different cross-lingual and multi-task strategies for training the pretrained language models. Through extensive experiments, we found that (a) cross-lingual/multi-task training, and (b) collecting an external balanced dataset, can benefit the genre and framing detection. We constructed ensemble models from the results and achieved the highest macro-averaged F1 scores in Italian and Russian genre categorization subtasks.
%R 10.18653/v1/2023.semeval-1.237
%U https://aclanthology.org/2023.semeval-1.237
%U https://doi.org/10.18653/v1/2023.semeval-1.237
%P 1702-1711
Markdown (Informal)
[Hitachi at SemEval-2023 Task 3: Exploring Cross-lingual Multi-task Strategies for Genre and Framing Detection in Online News](https://aclanthology.org/2023.semeval-1.237) (Koreeda et al., SemEval 2023)
ACL