  Automatic term extraction plays an essential role in domain language
understanding and several natural language processing downstream tasks. In this
paper, we propose a comparative study on the predictive power of
Transformers-based pretrained language models toward term extraction in a
multi-language cross-domain setting. Besides evaluating the ability of
monolingual models to extract single- and multi-word terms, we also experiment
with ensembles of mono- and multilingual models by conducting the intersection
or union on the term output sets of different language models. Our experiments
have been conducted on the ACTER corpus covering four specialized domains
(Corruption, Wind energy, Equitation, and Heart failure) and three languages
(English, French, and Dutch), and on the RSDO5 Slovenian corpus covering four
additional domains (Biomechanics, Chemistry, Veterinary, and Linguistics). The
results show that the strategy of employing monolingual models outperforms the
state-of-the-art approaches from the related work leveraging multilingual
models, regarding all the languages except Dutch and French if the term
extraction task excludes the extraction of named entity terms. Furthermore, by
combining the outputs of the two best performing models, we achieve significant
improvements.
