  Acronym extraction is the task of identifying acronyms and their expanded
forms in texts that is necessary for various NLP applications. Despite major
progress for this task in recent years, one limitation of existing AE research
is that they are limited to the English language and certain domains (i.e.,
scientific and biomedical). As such, challenges of AE in other languages and
domains is mainly unexplored. Lacking annotated datasets in multiple languages
and domains has been a major issue to hinder research in this area. To address
this limitation, we propose a new dataset for multilingual multi-domain AE.
Specifically, 27,200 sentences in 6 typologically different languages and 2
domains, i.e., Legal and Scientific, is manually annotated for AE. Our
extensive experiments on the proposed dataset show that AE in different
languages and different learning settings has unique challenges, emphasizing
the necessity of further research on multilingual and multi-domain AE.
