Hitachi at SemEval-2023 Task 3: Exploring Cross-lingual Multi-task Strategies for Genre and Framing Detection in Online News

Yuta Koreeda, Ken-ichi Yokote, Hiroaki Ozaki, Atsuki Yamaguchi, Masaya Tsunokake +1 more
3/3/2023
cs.CLcs.AI

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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.

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Cite this paper

@article{koreeda2023hitachi,
  title  = {Hitachi at SemEval-2023 Task 3: Exploring Cross-lingual Multi-task Strategies for Genre and Framing Detection in Online News},
  author = {Yuta Koreeda and Ken-ichi Yokote and Hiroaki Ozaki and Atsuki Yamaguchi and Masaya Tsunokake and Yasuhiro Sogawa},
  year   = {2023},
  eprint = {2303.01794},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2303.01794v2}
}

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