Shared Task 1 System Description : Exploring different approaches for multilingual tasks (2021)

Abstract: The aim of the CASE 2021 Shared Task 1 was to detect and classify socio-political and crisis event information at document, sentence, cross-sentence, and token levels in a multilingual setting, with each of these subtasks being evaluated separately in each test language. Our submission contained entries in all of the subtasks, and the scores obtained validated our research finding : That the multilingual element of the tasks should be embraced, so that modeling and training regimes use the multilingual nature of the tasks to their mutual benefit, rather than trying to tackle the different languages separately.

## introduction the case shared task 1 concerned news events that are in the scope of contentious politics and characterized by riots and social movements, denoted "glocon gold" @xcite . the aim of the shared task was to detect and classify socio-political and crisis event information at document, sentence, cross-sentence, and token levels in a multilingual setting: ## team organisation in order to efficiently allocate resources, separate, parallel research efforts were initially made towards each subtask, with periodic knowledge sharing taking place between subtasks. data issues with subtask 1 (whereby, due to copyright reasons, a significant number of the news articles were severely truncated in the dataset provided), our original approach to this subtask was abandoned, and the approach from subtask 2 was quickly redeployed towards subtask 1 in the late stages of the shared task test phase -hence the ordering herein of system descriptions. ## methods all subtask teams used off-the-shelf pre-trained models, and training was conducted only on the training data provided through the shared task (except as noted in subtask 3, where some additional public data was used). the key language models used for the subtasks were pre-trained models sourced from the hugging face library foot_0 :