In this report, we describe our ClassBases submissions to a shared task on
multilingual protest event detection. For the multilingual protest news
detection, we participated in subtask-1, subtask-2, and subtask-4, which are
document classification, sentence classification, and token classification. In
subtask-1, we compare XLM-RoBERTa-base, mLUKE-base, and XLM-RoBERTa-large on
finetuning in a sequential classification setting. We always use a combination
of the training data from every language provided to train our multilingual
models. We found that larger models seem to work better and entity knowledge
helps but at a non-negligible cost. For subtask-2, we only submitted an
mLUKE-base system for sentence classification. For subtask-4, we only submitted
an XLM-RoBERTa-base for token classification system for sequence labeling. For
automatically replicating manually created event datasets, we participated in
COVID-related protest events from the New York Times news corpus. We created a
system to process the crawled data into a dataset of protest events.

该研究报告描述了作者参加多语言抗议事件检测的共享任务中的 ClassBases 的提交。作者使用 XLM-RoBERTa-base、mLUKE-base 和 XLM-RoBERTa-large 进行微调来进行文件分类、句子分类和标记分类。通过训练多语言模型和使用实体知识，研究者们发现大模型似乎效果更好，但代价也更高。

CASE-2022 多语言抗议活动检测任务中的 ClassBases 模型：多语言抗议新闻检测和自动复制手动创建的事件数据集

ClassBases at CASE-2022 Multilingual Protest Event Detection Tasks: Multilingual Protest News Detection and Automatically Replicating Manually Created Event Datasets

The viral video documenting the killing of George Floyd by Minneapolis police
officer Derek Chauvin inspired nation-wide protests that brought national
attention to widespread racial injustice and biased policing practices towards
black communities in the United States. The use of social media by the Black
Lives Matter movement was a primary route for activists to promote the cause
and organize over 1,400 protests across the country. Recent research argues
that moral discussions on social media are a catalyst for social change. This
study sought to shed light on the moral dynamics shaping Black Lives Matter
Twitter discussions by analyzing over 40,000 Tweets geo-located to Los Angeles.
The goal of this study is to (1) develop computational techniques for mapping
the structure of moral discourse on Twitter and (2) understand the connections
between social media activism and protest.

该研究分析了地理位置为洛杉矶的超过 40,000 条 Twitter 推文，揭示了塑造 Black Lives Matter 社交媒体讨论的道德动态，并旨在为（1）映射 Twitter 上道德话语结构的计算技术和（2）了解社交媒体活动与抗议之间的联系做出贡献。