  Event Detection (ED) is the task of identifying and classifying trigger words
of event mentions in text. Despite considerable research efforts in recent
years for English text, the task of ED in other languages has been
significantly less explored. Switching to non-English languages, important
research questions for ED include how well existing ED models perform on
different languages, how challenging ED is in other languages, and how well ED
knowledge and annotation can be transferred across languages. To answer those
questions, it is crucial to obtain multilingual ED datasets that provide
consistent event annotation for multiple languages. There exist some
multilingual ED datasets; however, they tend to cover a handful of languages
and mainly focus on popular ones. Many languages are not covered in existing
multilingual ED datasets. In addition, the current datasets are often small and
not accessible to the public. To overcome those shortcomings, we introduce a
new large-scale multilingual dataset for ED (called MINION) that consistently
annotates events for 8 different languages; 5 of them have not been supported
by existing multilingual datasets. We also perform extensive experiments and
analysis to demonstrate the challenges and transferability of ED across
languages in MINION that in all call for more research effort in this area.
