@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.",
}

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.
Koreeda, Yuta Yokote, Ken-ichi Ozaki, Hiroaki Yamaguchi, Atsuki Tsunokake, Masaya Sogawa, Yasuhiro
Hitachi at SemEval-2023 Task 3: Exploring Cross-lingual Multi-task Strategies for Genre and Framing Detection in Online News