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

被引：0

作者：

Koreeda, Yuta ^{[1
]}

Yokote, Ken-ichi ^{[1
]}

Ozaki, Hiroaki ^{[1
]}

Yamaguchi, Atsuki ^{[1
]}

Tsunokake, Masaya ^{[1
]}

Sogawa, Yasuhiro ^{[1
]}

机构：

[1] Hitachi Ltd, Res & Dev Grp, Kokubunji, Tokyo, Japan

来源：

17TH INTERNATIONAL WORKSHOP ON SEMANTIC EVALUATION, SEMEVAL-2023 | 2023年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：1702 / 1711

页数：10

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