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- W4385570629 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." @default.
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- W4385570629 date "2023-01-01" @default.
- W4385570629 modified "2023-09-24" @default.
- W4385570629 title "Hitachi at SemEval-2023 Task 3: Exploring Cross-lingual Multi-task Strategies for Genre and Framing Detection in Online News" @default.
- W4385570629 doi "https://doi.org/10.18653/v1/2023.semeval-1.237" @default.
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