  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.
