Handshakes AI Research at CASE 2021 Task 1: Exploring different approaches for multilingual tasks
This work addresses multilingual event detection for researchers and practitioners, but it is incremental as it applies existing methods to a new shared task.
The paper tackled the CASE 2021 Shared Task 1 for multilingual socio-political and crisis event detection, finding that embracing multilingual modeling improved performance across subtasks, as validated by their submission scores.
The aim of the CASE 2021 Shared Task 1 (Hürriyetoğlu et al., 2021) 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 aspect 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. Our code is available at https://github.com/HandshakesByDC/case2021/