  Domain adaptive pretraining, i.e. the continued unsupervised pretraining of a
language model on domain-specific text, improves the modelling of text for
downstream tasks within the domain. Numerous real-world applications are based
on domain-specific text, e.g. working with financial or biomedical documents,
and these applications often need to support multiple languages. However,
large-scale domain-specific multilingual pretraining data for such scenarios
can be difficult to obtain, due to regulations, legislation, or simply a lack
of language- and domain-specific text. One solution is to train a single
multilingual model, taking advantage of the data available in as many languages
as possible. In this work, we explore the benefits of domain adaptive
pretraining with a focus on adapting to multiple languages within a specific
domain. We propose different techniques to compose pretraining corpora that
enable a language model to both become domain-specific and multilingual.
Evaluation on nine domain-specific datasets-for biomedical named entity
recognition and financial sentence classification-covering seven different
languages show that a single multilingual domain-specific model can outperform
the general multilingual model, and performs close to its monolingual
counterpart. This finding holds across two different pretraining methods,
adapter-based pretraining and full model pretraining.
