  We develop and evaluate multilingual scientific documents similarity
measurement models in this work. Such models can be used to find related works
in different languages, which can help multilingual researchers find and
explore papers more efficiently. We propose the first multilingual scientific
documents dataset, Open-access Multilingual Scientific Documents (OpenMSD),
which has 74M papers in 103 languages and 778M citation pairs. With OpenMSD, we
pretrain science-specialized language models, and explore different strategies
to derive "related" paper pairs to fine-tune the models, including using a
mixture of citation, co-citation, and bibliographic-coupling pairs. To further
improve the models' performance for non-English papers, we explore the use of
generative language models to enrich the non-English papers with English
summaries. This allows us to leverage the models' English capabilities to
create better representations for non-English papers. Our best model
significantly outperforms strong baselines by 7-16% (in mean average
precision).
