Persuasion techniques detection in news in a multi-lingual setup is
non-trivial and comes with challenges, including little training data. Our
system successfully leverages (back-)translation as data augmentation
strategies with multi-lingual transformer models for the task of detecting
persuasion techniques. The automatic and human evaluation of our augmented data
allows us to explore whether (back-)translation aid or hinder performance. Our
in-depth analyses indicate that both data augmentation strategies boost
performance; however, balancing human-produced and machine-generated data seems
to be crucial.

使用多语言转换器模型的数据扩充策略，探索向新闻中添加说服技巧的自动检测，应用于少量训练数据的情景下，有效提高系统性能，同时平衡人类和机器生成的数据对系统性能进行优化。

2023 SemEval 任务 3 中的 NAP：少即是多吗？(回) 译作为检测说服技巧的数据增强策略

NAP at SemEval-2023 Task 3: Is Less Really More? (Back-)Translation as  Data Augmentation Strategies for Detecting Persuasion Techniques

This paper describes our approach for SemEval-2023 Task 3: Detecting the
category, the framing, and the persuasion techniques in online news in a
multi-lingual setup. For Subtask 1 (News Genre), we propose an ensemble of
fully trained and adapter mBERT models which was ranked joint-first for German,
and had the highest mean rank of multi-language teams. For Subtask 2 (Framing),
we achieved first place in 3 languages, and the best average rank across all
the languages, by using two separate ensembles: a monolingual
RoBERTa-MUPPETLARGE and an ensemble of XLM-RoBERTaLARGE with adapters and task
adaptive pretraining. For Subtask 3 (Persuasion Techniques), we train a
monolingual RoBERTa-Base model for English and a multilingual mBERT model for
the remaining languages, which achieved top 10 for all languages, including 2nd
for English. For each subtask, we compare monolingual and multilingual
approaches, and consider class imbalance techniques.

研究使用多语言集成模型，针对 SemEval-2023 任务 3：在在线新闻中检测类别、框架和说服技巧。在三个子任务中，使用 RoBERTa 和 mBERT 等方法，分别在不同语言中获得前几名的结果。