  Dense retrieval models using a transformer-based bi-encoder design have
emerged as an active area of research. In this work, we focus on the task of
monolingual retrieval in a variety of typologically diverse languages using one
such design. Although recent work with multilingual transformers demonstrates
that they exhibit strong cross-lingual generalization capabilities, there
remain many open research questions, which we tackle here. Our study is
organized as a "best practices" guide for training multilingual dense retrieval
models, broken down into three main scenarios: where a multilingual transformer
is available, but relevance judgments are not available in the language of
interest; where both models and training data are available; and, where
training data are available not but models. In considering these scenarios, we
gain a better understanding of the role of multi-stage fine-tuning, the
strength of cross-lingual transfer under various conditions, the usefulness of
out-of-language data, and the advantages of multilingual vs. monolingual
transformers. Our recommendations offer a guide for practitioners building
search applications, particularly for low-resource languages, and while our
work leaves open a number of research questions, we provide a solid foundation
for future work.
