  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.
